Anthropic

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. Performance Engineer, Inference Engine About the Role Anthropic's inference engine is the software between the accelerator kernels and the routing layer. It manages the entire token path in between: batching requests, laying the model out across chips, managing memory for weights and activations, coordinating every forward pass, and managing model state across requests. Built in-house, it runs on all of our accelerator platforms, serving Claude to millions of users and running our research workloads. You will work on building and optimizing this system at Anthropic scale: improving throughput, cost, reliability, and latency across all accelerator and cloud platforms. You are intimately familiar with the hardware and bandwidth numbers (FLOPs, HBM, PCIe, RDMA, network links, etc.) and can model a problem quickly: where the time and bytes go, and what sets the bound. The role is deeply technical and high-impact, and suits engineers who enjoy working across accelerator programming, high-performance systems that seamlessly coordinate between host and device, and large-scale distributed systems. Familiarity with the transformer architecture is a plus. Some example recurring themes: - Keep device utilization high. Accelerators should never be waiting due to other overheads. - Reuse instead of recompute. Keep model state cached and reuse it whenever that is cheaper than computing it again. - Measure, model, then change. We build the observability to see where the gaps are, model the impact of potential improvements, deploy them, and go around again, with Claude speeding up every turn of that loop. - Tokens you can trust. Ensuring model quality matters more than efficiency. We build the infrastructure to ensure Claude maintains its intelligence across platforms and over time. - Safety on every token. We work closely with our safeguards and safety teams. The inference engine is the backbone behind our production safety systems, ensuring efficiency without compromising robustness. Minimum Qualifications - A working mental model of LLM inference: how prefill and decode land on an accelerator's compute, memory, and interconnect, and what the host is doing meanwhile - Proven quick learner: ramped fast in deep, unfamiliar systems and shipped consequential changes quickly - Strong systems programming (Rust, C++, or similar), with care for code quality and tests - Analytical about performance: observe and profile first, form a hypothesis, test it, then change the code and measure again - Low ego: ask the naive question, take feedback well, pick up slack outside your job description - Enjoy pair programming (we love to pair!) and care about the societal impacts of your work Preferred Qualifications - Experience inside an LLM serving engine and a sense of where its abstractions strain - GPU/Accelerator programming - OS internals - Language modeling with transformers - Experience building an allocator, cache, scheduler, or high-bandwidth transport - Fluency in Rust - Experience making systems reproducible: determinism, replay, property-based tests The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $350,000 - $850,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role Anthropic's Security Governance, Risk, and Compliance (GRC) team is the connective tissue that holds the company accountable to its security and control commitments. We translate regulatory, customer, and voluntary obligations into controls that teams act on, and give leadership a bird's-eye view of how well we're meeting them. We're building toward continuous assurance, to challenge and evidence the performance of controls continuously rather than through periodic audits. As Anthropic prepares for life as a public company, the Sarbanes-Oxley (SOX) control environment over our technology stack is one of the most consequential things this team owns. As part of Security GRC's technical controls assurance function, you will be the voice on what the IT general controls must achieve to support SOX 404 compliance. In partnership with Internal Audit, you will define control requirements and acceptance criteria for the in-scope engineering systems and infrastructure that underpin financial reporting. You will pair with engineering as they design and implement against those requirements, and validate that what ships actually meets the bar before Internal Audit and our external auditors test it. You are the product owner for control design methodology and continuous control monitoring, initially around ITGCs, but extending into other areas of security and compliance to drive visibility where and when we need it. Key responsibilities - Define control requirements and acceptance criteria across the core ITGC domains of logical access, change management, computer operations, and program development for SOX in-scope systems, including home-built platforms where the control has to be designed into the system rather than bolted on. - Set the bar for in-scope systems from day one. As financially significant systems are built, migrated, or replaced, define what the system must provide (auditability, segregation of duties, change control, immutable logging, evidence retention) before go-live, so controls are not retrofitted after the fact. - Pressure-test changes for SOX impact during design. Review major infrastructure, system, and agent framework changes for control impact while decisions are still cheap, and maintain a clear view of which changes alter the SOX scope, key control population, or evidence requirements. - Own second-line control monitoring and evidence readiness. Stand up continuous controls monitoring and automated evidence collection for ITGCs (control testing, walkthrough preparation, population and completeness validation, and mapping to the common controls framework). Materially raise automated evidence coverage and cut audit prep time. - Drive control deficiency remediation with cross functional partners. Track and root-cause ITGC deficiencies surfaced by monitoring, Internal Audit, or external audit; partner with engineering owners on remediation design; and assess whether remediation actually closes the gap before re-testing. - Assess scope changes through a SOX lens. When new products, entities, systems, or integrations come into scope, provide technical and compliance assessment of their impact on control design, evidence requirements, and engineering effort before commitments are made. - Maintain alignment with the broader compliance portfolio. Where SOX ITGCs overlap with SOC 2, ISO 27001/42001, or other frameworks, ensure controls are designed once and evidenced once, and that changes made for one framework do not silently break another. Minimum qualifications - Thrive at the pace of a hypergrowth company. You're comfortable making calls with incomplete information and reprioritizing as scope shifts. - Have led or been a senior contributor to an ITGC program through SOX 404 readiness and/or at a public company, with a working command of PCAOB AS 2201, COSO 2013, and how external auditors scope, test, and evaluate technology controls and deficiencies. - Have genuine engineering fluency, possibly from an earlier engineering career: you can read code and Terraform, follow a CI/CD pipeline end to end, and challenge a design on its technical merits. - Have programming skills in Python or at least one systems language such as Go, Rust, or C/C++. - Have deep familiarity with developer platform, release engineering, cloud infrastructure, or ERP/financial systems control domains. - Understand the role of the second line: you can advise and challenge engineering without taking ownership of their controls, and you know where the line sits between your monitoring and Internal Audit's independent testing. - Are a strong collaborator and communicator across Finance, Engineering, Internal Audit, and external auditors. - Use Claude and other LLMs as daily working tools, and have grounded, specific views on which SOX assurance workflows AI can run today and which it can't yet. - Translate SOX and framework language into acceptance criteria engineers can build against, and translate engineering reality back into assurance language auditors and leadership can rely on. - Default to getting the requirement designed into the system rather than papering over the gap with procedure. Preferred qualifications - A combination of audit or advisory experience (Big 4 or equivalent, ideally IT audit) with in-house experience at an AI-forward tech company, in either order. - Taken a company through a first-year SOX 404(a) and 404(b) assessment, including a first external ITGC audit. - Defined or assessed controls over home-built financially significant systems, usage-based billing, or revenue metering pipelines. - Defined or assessed controls for AI/ML systems or agents acting in production environments. - Stood up continuous controls monitoring or automated evidence programs. - Experience with SOC 1 reliance, service organization control mapping, and complementary user entity controls. - CISSP, CISA, CPA, or equivalent certification. The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $410,000 - $510,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role: The Safeguards ML Infra team designs, builds, and operates the production infrastructure that powers Claude's safety systems. We own the critical backend services that ensure safety on the token generation path, and we own the operational work of getting those systems safely into production: standing up safeguards for every new model launch, and deploying new safety classifiers as they ship. Every frontier model release runs through this team – we configure, verify, and roll out safeguards across every platform Claude runs on (1P, AWS Bedrock, GCP Vertex, etc.), and we lead incident response when issues arise. This role sits at the center of that operational work. You'll ensure safeguards are properly configured and deployed for model launches and own the off-cycle deployment of new safety classifiers — canarying changes, verifying that the right safeguards are provably live on the right models, and holding rollback authority when something looks wrong. Every launch should also shrink the checklist, and the manual verifications should evolve into a system that runs itself. You'll turn launch runbooks into tooling, hand-built checks into continuous validation, and one-off deploys into a repeatable pipeline. We're looking for engineers with deep experience in production change management at scale — people who have owned deploy pipelines, config management systems, rollout safety, or launch readiness for systems under real production pressure. Familiarity with ML research or transformer architectures is not required — you will learn that on the job. What we prioritize is production judgment: a track record of shipping changes to critical systems safely, and of automating yourself out of the work you did last quarter. What you'll do: - Launch captain model releases: stand up, configure, and verify safeguards for every new model, and serve as the safeguards point of contact in the launch room during release windows. - Own the off-cycle deployment of new safety classifiers as they ship from research — canarying rollouts, running post-deploy validations, and investigating discrepancies when something looks wrong. - Verify that the right safeguards are provably live on the right models across every deployment platform (1P, AWS Bedrock, GCP Vertex, etc.), and detect and eliminate configuration drift between them. - Automate yourself out of last quarter's work: turn launch runbooks into tooling, hand-built checks into continuous validation, and one-off deploys into a repeatable pipeline. - Plan to use Claude aggressively to do this! And be a trailblazer that paves the path for safe agentic operations of safety-critical systems. - Build and maintain a safeguards registry with full provenance — what is running in production, on which model, on which platform, and when and by whom it was deployed. - Participate in on-call and operational-duty rotations covering service incidents, model provisioning, and time-sensitive research and safety launches. You may be a good fit if you: - Have owned production change management at scale — deploy pipelines, config management systems, canary analysis — and have strong opinions about what "verified" means. - Have run high-stakes releases: served as a launch captain, incident commander, or release owner for systems where a bad deploy has real consequences, and are energized rather than drained by being in the critical path. - Have meaningful on-call experience for production systems, including incident response and postmortem-driven improvements — and a track record of turning (and fixing!) postmortem action items into process and tooling changes. - Have a desire to close the gap where nobody has yet raised their hand, even if it requires manually hand-holding processes until automation and tooling can be built. - Have hands-on experience deploying and operating on cloud platforms (AWS, GCP) at scale. - Are proficient in Python; experience with Rust is a plus but not required. Strong candidates may also have: - 8+ years of industry software engineering or site reliability engineering experience. - A demonstrated history of reducing operational toil through automation, including transitioning teams from manual deployment processes to self-serve pipelines. - Experience running launch or production-readiness review processes across multiple teams. - Familiarity with LLM inference systems and the operational characteristics of transformer-based models. The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $320,000 - $485,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role The Security Engineering team's mission is to safeguard our AI systems and maintain the trust of our users and society at large. Whether we're developing critical security infrastructure, building secure development practices, or partnering with our research and product teams, we are committed to operating as a world-class security organization and keeping the safety and trust of our users at the forefront of everything we do. Responsibilities: - Build security for large-scale AI clusters across multiple clouds and bare-metal data centers, including IAM, network segmentation and encryption controls - Design and implement network security controls: default-deny ingress and egress, segmentation between research, training and production environments, private connectivity across clouds and data centers, cloud firewall policy and mTLS with service identity - Ship these controls as secure defaults using infrastructure as code and GitOps workflows, so new clusters, VPCs and data center links inherit the right boundaries - Build visibility into network traffic and boundary drift, with detection for exfiltration paths and automated remediation where it is safe to do so - Design secure-by-design development workflows and CI/CD pipelines across our services, with expertise in Kubernetes security, container orchestration and identity management - Threat model and assess risk for complex multi cloud and networked environments, and join infrastructure networking, cluster and data center teams at design time so topology, routing and firewall decisions carry our security requirements from day one - Mentor engineers and contribute to hiring and growth of the Security team You may be a good fit if you: - Have 8-15+ years of software engineering experience implementing and maintaining critical systems at scale - Strong software engineering skills in Python or at least one systems language (Go, Rust, C/C++) - Experience implementing and operating critical systems at scale using DevOps and cloud automation practices such as infrastructure as code - Hands-on network security engineering in cloud or on-prem environments, for example VPC design, cloud firewall policy, egress control, private service connectivity or network segmentation - Working knowledge of Kubernetes security and networking, including network policy, service identity and container hardening - Experience with threat modeling and risk assessment for networked and multi cloud systems - Track record of driving engineering excellence through high standards, constructive code reviews and mentorship - Clear communicator who can translate technical concepts across organizational levels and bring clarity and ownership to ambiguous technical problems - Low ego, high empathy engineer who attracts talent and supports diverse, inclusive teams - Experience supporting fast-paced startup engineering teams Strong candidates may also have experience with: - Secured Kubernetes networking at scale (CNI, network policy, service mesh or mTLS rollouts) or worked in high-assurance environments where controls had to be evidenced, not just deployed - Worked on interconnects and private connectivity between cloud providers and physical data centers, including management-plane and out-of-band network security - Built network traffic visibility or exfiltration detection systems - Exposure to large-scale distributed training and what it demands of a network - Led cross-functional security initiatives and navigated complex organizational dynamics - Managing infrastructure through automated configuration and policy enforcement - Hardening containerized applications and enforcing security policies The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: £255,000 - £325,000 GBP Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Role Anthropic is at the forefront of AI research, dedicated to developing safe, ethical, and powerful artificial intelligence. Our mission is to ensure that transformative AI systems are aligned with human interests. We are seeking a Staff level Engineer to join our Pre-training team, responsible for developing the next generation of large language models. In this role, you will work at the intersection of cutting-edge research and practical engineering, contributing to the development of safe, steerable, and trustworthy AI systems. Responsibilities - Design and implement data processing infrastructure for large language model training (highly performant, reproducible, traceable) - Develop and maintain core processing primitives (e.g., tokenization, deduplication, chunking) with a focus on scalability - Build robust systems for data quality assurance and validation at scale - Collaborate with research teams to implement novel data processing architectures - Build and operate end-to-end data pipelines that turn raw web-scale corpora into training-ready datasets You may be a good fit if you have: - 5+ YOE outside of internships - Strong software engineering skills with experience building high-throughput fault-tolerant distributed systems - Hands-on experience with distributed computing frameworks, particularly Apache Spark - Excellent problem-solving skills and attention to detail - Strong communication skills and ability to work in a collaborative environment - Advanced degree in Computer Science or related field - Experience with language model training infrastructure - Background in Data Infrastructure, MLOps, or ML infrastructure Strong candidates may have: - Have significant experience building high-throughput fault-tolerant distributed systems - Expertise with Python and Rust - Passionate about system reliability and performance - Are comfortable working with ambiguous requirements and evolving specifications - Take ownership of problems and drive solutions independently - Are excited about contributing to the development of safe and ethical AI systems - Can balance technical excellence with practical delivery - Are eager to learn about machine learning research and its infrastructure requirements Sample Projects - Designing and implementing distributed computing architecture for web-scale data processing - Building scalable infrastructure for model training data preparation - Developing fault-tolerant distributed processing systems - Implementing new infrastructure components based on research requirements The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $500,000 - $850,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.