About the role

We are seeking an Operations Manager to build and lead a new pillar of our Platform Operations function: Community Operations & Executive Escalations. This role will stand up the team, processes, and infrastructure that protect Anthropic's reputation when high-stakes user issues surface on social media or arrive through executive channels. You'll own end-to-end escalation management - detection, triage, incident coordination, and resolution- across two distinct but related workstreams: brand-impacting conversations on public social channels and high-sensitivity inbound from Anthropic employees on behalf of users. The ideal candidate combines calm-under-fire incident judgment with the operational rigor to build durable systems.

Responsibilities:

• Hire, lead, and develop a high-performing team of Community Operations specialists, covering detection through resolution
• Build the function from zero: stand up social listening infrastructure, define escalation criteria and severity tiers, author the incident management SOP, and establish coverage hours and on-call schedules
• Own social monitoring across public channels - triaging brand mentions and running escalations to resolution
• Own executive escalations end to end: receive inbound from Anthropic executives on behalf of users, lead your team in quarterbacking investigation and coordinate remediation across partner teams
• Serve as the central coordination point during live escalations and drive public/private response decisions to closure
• Build clear operating models with cross-functional teams so every stakeholder knows exactly where to send an escalation and what happens next
• Define and report on success metrics (time to triage, time to resolution, escalation volume by source) and run post-incident reviews that feed learnings back into criteria and process
• Develop response templates and playbooks for common scenarios so the team can move fast without sacrificing quality or judgment
• Provide thoughtful coaching and feedback to your direct reports, and partner with them on their career development and growth

You may be a good fit if you have:

• 8+ years of experience in community operations, escalations management, or a related field - ideally in a consumer or developer platform environment
• 5+ years of people management experience, preferably in a fast-growing technology company
• Direct experience running incident or escalation workflows: you've been the person managing the channel, paging the on-call, and writing the post-incident report
• Familiarity with social listening tooling and the dynamics of how issues spread across public social platforms
• Excellent judgment under pressure in reading a fast-moving situation, separating signal from noise, and making calls with incomplete information
• Proven track record of building operational programs from 0 to 1: writing the SOP, defining the criteria, standing up the tooling, and then iterating as reality breaks your assumptions
• Strong cross-functional influence and ability to align teams around a shared plan in real time, even when incentives differ
• Clear, concise written communication - you can brief an executive in three sentences and document an incident so the next person can learn from it
• Comfort with ambiguity and a bias toward action; this team doesn't exist yet and you'll be defining what good looks like
• Excitement about protecting Anthropic's reputation and relationships at a moment when public attention on AI has never been higher

About the role

We are seeking a Staff Software Engineer to build and operate the safety, oversight, and intervention mechanisms that protect Claude on third-party cloud service provider (CSP) platforms. As the engineer responsible for Safeguards on those surfaces, you will ensure that every request served through our CSP partners is monitored for misuse, enforced against policy, and compliant with the data residency and privacy commitments that enterprise CSP customers expect.

You will sit at the seam between the Safeguards organization and the Cloud Inference team: taking classifiers, detection signals, and enforcement policies developed by Safeguards and making them run reliably inside a CSP partner’s infrastructure at serving-path latency and scale. You will own the architecture that lets our safeguards operate within those constraints without gaps. You will build, deploy and operate the multi-layered defenses that catch unwanted model behavior in real time, the telemetry pipelines that give us situational awareness over CSP traffic, and the enforcement hooks that let us act quickly when something goes wrong. Your work will directly determine whether Anthropic can ship frontier models on CSP platforms at the same safety bar we hold ourselves to on our first-party API.

Responsibilities:

• Build, deploy and operate real-time safeguards infrastructure—classifiers, rate limits, enforcement actions, and intervention hooks—embedded directly in the third-party CSP inference serving path
• Design and maintain the data residency and privacy architecture for safeguards signals on CSP platforms, ensuring we can detect abuse and monitor model behavior while honoring regionalization boundaries and enterprise contractual commitments
• Develop telemetry, logging, and evaluation pipelines that give Safeguards, Policy, and T&S operational teams situational awareness over CSP traffic and close the visibility gap between third-party and first-party serving
• Dive into the CSP serving stack to identify the lowest-impact points to gather signals or introduce interventions without degrading latency, stability, or overall architecture
• Hold a high operational bar: own on-call, drive root-cause analyses and postmortems for safeguards incidents on CSP platforms, and build systems that reduce the human intervention required to keep Claude safe
• Work closely with Safeguards research, Policy & Enforcement, the Cloud Inference team, and CSP partner contacts to turn detection research and policy decisions into production enforcement that works inside a partner’s cloud.

You may be a good fit if you:

• Have a Bachelor’s degree in Computer Science, Software Engineering, or comparable experience
• Have 7+ years of experience in high-scale, high-reliability software development, ideally with exposure to trust & safety, anti-abuse, fraud, or integrity systems
• Are proficient in Python and comfortable working across the stack—from request-path services to data pipelines to internal tooling
• Think adversarially: you can see a system from a bad actor’s perspective, anticipate how they will respond to countermeasures, and design defenses in depth rather than single points of enforcement
• Have experience scaling infrastructure to accommodate rapid traffic growth while keeping latency and reliability within tight budgets
• Are deeply interested in the potential transformative effects of advanced AI systems and are committed to ensuring their safe development
• Have strong communication skills and can explain complex technical and risk tradeoffs to non-technical stakeholders across Policy, Legal, and partner organizations
• Enjoy working in a fast-paced, early environment; comfortable with adapting priorities as driven by the rapidly evolving AI space

Strong candidates may also have experience with:

• Building trust and safety, anti-spam, fraud, or abuse detection and mitigation mechanisms for AI/ML systems, or the infrastructure to support these systems at scale
• Machine learning serving infrastructure (GPUs/TPUs, inference servers, load balancing) and the operational realities of running models in production
• Major cloud platform internals—IAM, Network/service perimeter controls, regional resource constraints, cloud-native logging/monitoring—or experience shipping software that runs inside a partner’s cloud rather than your own
• Data residency, privacy engineering, or compliance-constrained architectures, particularly where telemetry has to stay within regional or contractual boundaries
• Working closely with operational and human-review teams to build custom internal tooling, admin UX, and alerting
• Adversarial mindset: has shipped defenses against motivated attackers before, knows what it feels like when they adapt, and can sprint to close a gap before it becomes an incident
• Comfortable operating at the intersection of platform/infra engineering and trust & safety—neither a pure infra engineer nor a pure T&S engineer, but someone who can credibly do both
• Has shipped software that runs inside someone else’s infrastructure (partner cloud, embedded deployment, or similar) and knows how to get things done when you don’t control the whole stack
• Senior enough to own a cross-team seam independently, drive consensus across orgs, and make latency/safety tradeoff calls without escalation
• TypeScript or Rust, and agentic coding tools such as Claude Code

Responsibilities:

• Designing and implementing from scratch new information architecture strategies
• Performing finetuning and reinforcement learning to teach language models how to interact with new information architectures
• Building “hard” knowledge base eval sets to help identify failure modes of how language models work with external data
• Designing and evaluating advanced agentic search capabilities.

You may be a good fit if you:

• Are a very experienced Python programmer who can quickly produce reliable, high quality code that your teammates love using
• Have good machine learning research experience 
• Have experience developing software that utilizes Large Language Models such as Claude
• Are results-oriented, with a bias towards flexibility and impact
• Pick up slack, even if it goes outside your job description
• Enjoy pair programming (we love to pair!)
• Want to partner with world-class ML researchers to develop new LLM capabilities
• Care about the societal impacts of your work
• Have clear written and verbal communication

Strong candidates will also have experience with:

• Collaborating with product teams to quickly prototype and deliver innovative solutions
• Building complex agentic systems that utilize LLMs
• Developing scalable distributed information retrieval systems, such as search engines, knowledge graphs, RAG, indexing, ranking, query understanding, and distributed data processing

About the Role

Our mandate is to make inference deployment boring and unattended.

Anthropic serves Claude to millions of users across GPUs, TPUs, and Trainium — and every model update must reach production safely, quickly, and without disrupting service. We're building the systems that make inference deployment continuous and unattended.

As a Software Engineer on the Launch Engineering team, you'll design and build the deployment infrastructure that moves inference code from merge to production. This is a resource-constrained optimization problem at its core: validation and deployment consume the same accelerator chips that serve customer traffic — your deploys compete with live user requests for the same hardware. Every model brings different fleet sizes, startup times, and correctness requirements, so the system must adapt continuously. You'll build systems that navigate these constraints — orchestrating validation, scheduling deployments intelligently, and driving down cycle time from merge to production.

If you've built deployment systems at scale and gravitate toward the hardest problems at the intersection of automation and resource management, this team will give you an outsized scope to work on them.

Responsibilities

• Own deployment orchestration that continuously moves validated inference builds into production across GPU, TPU, and Trainium fleets, unattended under normal conditions
• Improve capacity-aware deployment scheduling to maximize deployment throughput against constrained accelerator budgets and variable fleet sizes
• Extend deployment observability — dashboards and tooling that answer "what code is running in production," "where is my commit," and "what validation passed for this deploy"
• Drive down cycle time from code merge to production with pipeline architectures that minimize serial dependencies and maximize parallelism
• Optimize fleet rollout strategies for large-scale deployments across thousands of GPU, TPU, and Trainium chips, minimizing disruption to serving capacity
• Evolve self-service model onboarding so that new models can be added to the continuous deployment pipeline without Launch Engineering involvement
• Partner across the Inference organization with teams owning validation, autoscaling, and model routing to integrate deployment automation with their systems

You May Be a Good Fit If You Have

• 5+ years of experience building deployment, release, or delivery infrastructure at scale
• Strong software engineering skills with experience designing systems that manage complex state machines and multi-stage pipelines
• Experience with deployment systems where resource constraints shape the design — whether that's fleet capacity, network bandwidth, hardware availability, or coordinated rollout windows
• A track record of building automation that measurably improves deployment velocity and reliability
• Proficiency with Kubernetes-based deployments, rolling update mechanics, and container orchestration
• Comfort working across the stack — from backend services and databases to CLI tools and web UIs
• Strong communication skills and the ability to work closely with oncall engineers, model teams, and infrastructure partners

Strong Candidates May Also Have

• Experience with ML inference or training infrastructure deployment, particularly across multiple accelerator types (GPU, TPU, Trainium)
• Background in capacity planning or resource-constrained scheduling (e.g., bin-packing, fleet management, job scheduling with hardware affinity)
• Experience with progressive delivery in systems with long validation cycles: canary/soak testing, blue-green deployments, traffic shifting, automated rollback
• Experience at companies with large-scale release engineering challenges (mobile release trains, monorepo deployments, multi-datacenter rollouts)
• Experience with Python and/or Rust in production systems

You may be a good fit if you:

• Have significant software engineering experience, particularly with distributed systems
• Are results-oriented, with a bias towards flexibility and impact
• Pick up slack, even if it goes outside your job description
• Enjoy pair programming (we love to pair!)
• Want to learn more about machine learning systems and infrastructure
• Thrive in environments where technical excellence directly drives both business results and research breakthroughs
• Care about the societal impacts of your work

Strong candidates may also have experience with:

• High-performance, large-scale distributed systems
• Implementing and deploying machine learning systems at scale
• Load balancing, request routing, or traffic management systems
• LLM inference optimization, batching, and caching strategies
• Kubernetes and cloud infrastructure (AWS, GCP, Azure)
• Python or Rust

Representative projects:

• Designing intelligent routing algorithms that optimize request distribution across thousands of accelerators
• Autoscaling our compute fleet to dynamically match supply with demand across production, research, and experimental workloads
• Building production-grade deployment pipelines for releasing new models to millions of users
• Integrating new AI accelerator platforms to maintain our hardware-agnostic competitive advantage
• Contributing to new inference features (e.g., structured sampling, prompt caching)
• Supporting inference for new model architectures
• Analyzing observability data to tune performance based on real-world production workloads
• Managing multi-region deployments and geographic routing for global customers

Deadline to apply: None. Applications will be reviewed on a rolling basis. 

About the Role

The Cloud Inference team scales and optimizes Claude to serve the massive audiences of developers and enterprise companies across AWS, GCP, Azure, and future cloud service providers (CSPs). We own the end-to-end product of Claude on each cloud platform—from API integration and intelligent request routing to inference execution, capacity management, and day-to-day operations.

Our engineers are extremely high leverage: we simultaneously drive multiple major revenue streams while optimizing one of Anthropic's most precious resources—compute. As we expand to more cloud platforms, the complexity of managing inference efficiently across providers with different hardware, networking stacks, and operational models grows significantly. We need engineers who can navigate these platform differences, build robust abstractions that work across providers, and make smart infrastructure decisions that keep us cost-effective at massive scale.

Your work will increase the scale at which our services operate, accelerate our ability to reliably launch new frontier models and innovative features to customers across all platforms, and ensure our LLMs meet rigorous safety, performance, and security standards.

What You'll Do

• Design and build infrastructure that serves Claude across multiple CSPs, accounting for differences in compute hardware, networking, APIs, and operational models
• Collaborate with CSP partner engineering teams to resolve operational issues, influence provider roadmaps, and stand up end-to-end serving on new cloud platforms
• Design and evolve CI/CD automation systems, including validation and deployment pipelines, that reliably ship new model versions to millions of users across cloud platforms without regressions 
• Design interfaces and tooling abstractions across CSPs that enable cost-effective inference management, scale across providers, and reduce per-platform complexity
• Contribute to capacity planning and autoscaling strategies that dynamically match supply with demand across CSP validation and production workloads
• Optimize inference cost and performance across providers—designing workload placement and routing systems that direct requests to the most cost-effective accelerator and region
• Contribute to inference features that must work consistently across all platforms
• Analyze observability data across providers to identify performance bottlenecks, cost anomalies, and regressions, and drive remediation based on real-world production workloads

You May Be a Good Fit If You:

• Have significant software engineering experience, with a strong background in high-performance, large-scale distributed systems serving millions of users
• Have experience building or operating services on at least one major cloud platform (AWS, GCP, or Azure), with exposure to Kubernetes, Infrastructure as Code or container orchestration
• Have strong interest in inference
• Thrive in cross-functional collaboration with both internal teams and external partners
• Are a fast learner who can quickly ramp up on new technologies, hardware platforms, and provider ecosystems
• Are highly autonomous and self-driven, taking ownership of problems end-to-end with a bias toward flexibility and high-impact work
• Pick up slack, even when it goes outside your job description

Strong Candidates May Also Have Experience With

• Direct experience working with CSP partner teams to scale infrastructure or products across multiple platforms, navigating differences in networking, security, privacy, billing, and managed service offerings
• A background in building platform-agnostic tooling or abstraction layers that work across cloud providers
• Hands-on experience with capacity management, cost optimization, or resource planning at scale across heterogeneous environments
• Strong familiarity with LLM inference optimization, batching, caching, and serving strategies
• Experience with Machine learning infrastructure including GPUs, TPUs, Trainium, or other AI accelerators
• Background designing and building CI/CD systems that automate deployment and validation across cloud environments
• Solid understanding of multi-region deployments, geographic routing, and global traffic management
• Proficiency in Python or Rust

About the Role

AIRE (AI Reliability Engineering) partners with teams across Anthropic to improve reliability across our most critical serving paths -- every hop from the SDK through our network, API layers, serving infrastructure, and accelerators and back. We jump into the trenches alongside partner teams to make the systems that deliver Claude more robust and resilient, be it during an incident or collaborating on projects. 

Reliability here is an emergent phenomenon that transcends any single team's boundaries, so someone has to zoom out and look at the whole picture. That's us -- and it means few teams at Anthropic offer this kind of dynamic, cross-cutting exposure to the systems that matter most.

Responsibilities:

• Develop appropriate Service Level Objectives for large language model serving systems, balancing availability and latency with development velocity
• Design and implement monitoring and observability systems across the token path
• Assist in the design and implementation of high-availability serving infrastructure across multiple regions and cloud provider
• Lead incident response for critical AI services, ensuring rapid recovery, thorough incident reviews, and systematic improvements
• Support the reliability of safeguard model serving -- critical for both site reliability and Anthropic's safety commitments.

You may be a good fit if you:

• Have strong distributed systems, infrastructure, or reliability backgrounds -- we're looking for reliability-minded software engineers and SREs
• Are curious and brave -- comfortable jumping into unfamiliar systems during an incident and helping drive resolution even when you don't have deep expertise yet
• Think holistically about how systems compose and where the seams are
• Can build lasting relationships across teams -- our engagement model depends on being welcomed as teammates, not outsiders with opinions
• Care about users and feel ownership over outcomes, even for systems you don't own
• Have excellent communication and collaboration skills -- you'll be partnering across the entire company
• Bring diverse experience -- the team's strength comes from people who've built product stacks, scaled databases, run massive distributed systems, and everything in between.

Strong candidates may also:

• Have been an SRE, Production Engineer, or in similar reliability-focused roles on large scale systems
• Have experience operating large-scale model serving or training infrastructure (>1000 GPUs)
• Have experience with one or more ML hardware accelerators (GPUs, TPUs, Trainium)
• Understand ML-specific networking optimizations like RDMA and InfiniBand
• Have expertise in AI-specific observability tools and frameworks
• Have experience with chaos engineering and systematic resilience testing
• Have contributed to open-source infrastructure or ML tooling.

About the UX research team

UX research at Anthropic is a small team with outsized leverage. We work across products used by millions of people—and on interactions that don't have established conventions yet.

We inform critical decisions through rigorous research, with stakeholders who eagerly incorporate findings into their work. Research has a seat at the table for product strategy, and we partner closely with design, product, and engineering. We combine qualitative and quantitative methods to understand user needs in a space that's evolving faster than anyone can track.

We move fast, favor impact over polish, and increasingly build our own tools—often with AI doing the heavy lifting—to do research at the speed this environment demands.

About this role

This is a senior individual-contributor role. You'll own research for a complex, fast-moving product area where the surface is expanding quickly and the most important user questions haven't been articulated yet, let alone answered.

You won't work from a fixed research roadmap. You'll prioritize constantly—making clear, well-informed bets about what to learn next, executing on them fast, and pivoting the moment the picture changes. Our product teams are deeply technical and move on instinct; your job is to make that instinct better-calibrated without slowing it down.

Roles here are fluid. On any given week you might be the researcher, the analyst, the one framing the next product bet, or the one building a prototype because that's the fastest way to find out.

Where this role lands depends on team needs and your strengths—we'll figure that out together.

Responsibilities

• Set the research agenda for a product area where the strategic questions are still fuzzy. Define what we need to learn, in what order, and why—then go learn it. Don't wait to be asked
• Design and run mixed-methods research at whatever fidelity the moment calls for. Some of it will be careful, foundational work the team references for a year. Some of it will be a finding you ship the same day the question came up
• Keep product teams close to users in a rigorous way—building the channels, cadence, and evidence base that make user reality hard to ignore
• Get hands-on with product data. Pull your own samples, build your own dashboards, instrument what's missing
• Prototype when prototyping is the fastest path to an answer—a research tool, a survey pipeline, a scrappy version of a feature to put in front of users
• Deeply leverage AI in how you work—using Claude to make yourself dramatically more efficient at analysis, synthesis, tooling, and everything in between
• Use what you study. Dogfood our products hard enough to develop your own informed opinions, and bring that texture into your research
• Anticipate where the product is heading before anyone asks. Show up with the user understanding the team is about to need
• Turn what you learn into recommendations confident enough to redirect a roadmap—including recommending we kill something that isn't working
• Build durable understanding, not just findings. The goal is that PMs, designers, and engineers internalize the user mental model well enough to make good calls when you're not in the room
• Develop new research methods when the existing playbook doesn't fit—particularly methods that use AI to do things that weren't possible before

Qualifications

• Industry UX research experience, including ownership of research strategy for a product or product area
• Track record of research that shifted product direction at the strategy level, not just the feature level
• Deep mixed-methods expertise—qualitative and quantitative
• Enough data fluency to use Claude to work directly with our data warehouse—pulling your own samples, writing your own queries, running your own analyses
• Demonstrated technical range: you've built research tooling, prototyped, scripted analyses, or otherwise shown you can keep up with very technical teams without translation overhead
• Advanced, hands-on use of AI in your own research workflow—you're already taking full advantage of its capabilities to enhance how you work
• Experience embedding with engineering-led product teams that ship fast, and earning influence in that environment
• Excellent storytelling, calibrated to audience. You can discuss a finding with an exec as effectively as with an engineer

About the Role

We are seeking a GRC Automation Lead to join our GRC organization and build the technical foundation for how we scale our risk and compliance programs. In this role, you will lead the team that designs and implements automated workflows, data pipelines, and integrations that transform manual compliance processes into scalable engineering systems. 

This is a greenfield opportunity to establish the team, architecture, and integrations that will define how we approach governance, risk, and compliance at Anthropic. The core challenge is a data problem: compliance information lives across dozens of systems—cloud infrastructure, identity providers, HR platforms, ticketing tools, code repositories—and your job is to design systems that bring it together, normalize it, and make it actionable. Success in this role comes from understanding how systems connect and how data flows between them, not from writing code yourself.

At Anthropic, you'll also have a unique advantage: the ability to design AI-powered workflows where Claude acts as an extension of your team, handling tasks that would traditionally require additional headcount or manual effort. You'll need ingenuity to identify where agentic AI can accelerate evidence collection, interpret unstructured data, triage compliance gaps, and augment human judgment in risk assessments. Working closely with Security, IT, and Engineering teams, you'll translate compliance and regulatory requirements into solutions that support audit programs including SOC 2, ISO, HIPAA, and FedRAMP, building systems that combine traditional automation with AI capabilities to achieve scale that wouldn't otherwise be possible.

Responsibilities: 

• Lead the team that establishes foundational GRC processes and architecture. Design and build automated workflows for risk management and compliance, creating scalable systems that enable continuous monitoring as Anthropic grows.
• Build data pipelines that aggregate risk, control, and asset information from across our technology stack. This means solving hard data integration problems: mapping disparate schemas, handling inconsistent data quality, and creating unified views of compliance posture through dashboards and reporting tools.
• Inform GRC platform strategy and implementation: in partnership with other programs, evaluate, select, and deploy tooling that meets our compliance requirements.
• Translate written policies and compliance requirements into policy-as-code—working with Engineering and Security teams to express requirements as enforceable rules, automated checks, and continuous validation rather than static documents.
• Establish feedback loops between policy and implementation: surface where technical controls diverge from written requirements, identify where policies need to evolve based on infrastructure realities, and ensure that compliance requirements are expressed in terms engineers can act on.
• Design and deploy agentic AI workflows that extend team capacity, using Claude to serve as a virtual GRC analyst to automate evidence analysis, monitor control effectiveness, draft audit responses, interpret policy documents, and handle other tasks that require reasoning over unstructured information.
• Design and maintain integrations connecting GRC tooling with cloud infrastructure, identity management systems, HRIS platforms, ticketing systems, version control, and CI/CD pipelines—working with engineers to implement integrations that enable automated evidence collection and continuous compliance validation.
• Build and lead an AI-forward GRC engineering function as we scale: hiring team members, establishing practices, and defining the technical roadmap for governance and compliance automation at Anthropic.

You may be a good fit if you:

• Have 12+ years of total experience and 3-4+ years of experience managing technical individual contributors or systems-focused teams, with a proven track record of building or scaling small teams (2-5 people) in security, compliance, automation, or operations functions.
• Are a systems thinker first. You understand how complex environments work: how data flows between systems, where integration points exist, what breaks when systems don't talk to each other. Your strength is designing the right architecture and environment for security monitoring, not necessarily implementing it yourself.
• Have 5+ years of experience designing automated workflows, data pipelines, or system integrations, whether through traditional development, low-code platforms, GRC tools, or process automation. We care about your ability to solve integration problems more than your programming language proficiency.
• Have a relentless focus on data integration: you understand how to pull data from multiple sources, normalize it, join it meaningfully, and surface insights. You're comfortable reasoning about messy, inconsistent data and designing systems that handle edge cases gracefully.
• Understand APIs and integration patterns: REST APIs, webhooks, authentication flows, polling vs. push architectures, and can evaluate systems based on how well they expose data and support automation.
• Can work independently with minimal guidance, taking ownership of complex problems from design through implementation while managing ambiguity inherent in early-stage programs.
• Have strong analytical and problem-solving skills with attention to detail necessary for compliance work, balanced with pragmatism about risk-based prioritization in fast-paced environments.

Strong candidates may have:

• Experience designing or implementing AI-powered automation, agentic workflows, or LLM-based tooling in operational contexts.
• Experience with GRC platforms such as ServiceNow GRC, Vanta, Drata, OneTrust, RSA Archer, or similar tools including configuration, customization, and integration capabilities
• Familiarity with scripting languages (Python or similar) for automation tasks, API interactions, and data transformation.
• Prior experience in high-growth startup environments demonstrating ability to build scalable processes and adapt quickly to changing requirements and priorities
• Familiarity with Infrastructure as Code tools (Terraform, CloudFormation, Ansible) and DevSecOps practices including CI/CD pipeline integration and policy-as-code implementations.
• Familiarity with cloud platforms (AWS, GCP, Azure) and an understanding of how compliance-relevant data can be extracted from their APIs and logging systems.

Deadline to apply: None, applications will be received on a rolling basis.

About the role

As a Product Manager for API Growth, you’ll drive growth for the Claude Platform (platform.claude.com) – the surface where developers and businesses build directly on Claude. Your efforts will span the entire funnel, driving growth of key API metrics across acquisition, activation, and monetization. You'll work closely with a cross-functional team of engineers, designers, marketers, and data scientists to develop and execute strategies that accelerate our growth while maintaining our commitment to safety and beneficial AI.

Responsibilities:

• Develop and execute a product strategy focused on driving Claude Platform growth metrics such as acquisition, activation, monetization
• Lead the ideation, development, and launch of growth features across our API product – onboarding, console, docs, billing, and self-serve upgrade paths
• Analyze product metrics and user feedback to identify opportunities and optimize performance
• Collaborate with engineering, design, developer relations, and marketing teams to deliver high-impact growth initiatives
• Conduct user research to understand customer needs and pain points
• Define and track key performance indicators (KPIs) for API growth
• Balance rapid iteration with our commitment to AI safety and ethics

Qualifications:

• 6+ years of product management experience, majority in growth focused roles
• Experience driving growth for API, developer platform, or technical / usage-based products
• Strong analytical skills, and experience with A/B testing and funnel optimization
• Excellent communication and stakeholder management skills
• Ability to thrive in a fast-paced, ambiguous environment
• Passion for AI technology and its potential impact on society
• Technical background or ability to work effectively with engineering teams
• Founder experience is a plus

About the role

As a Product Manager for Monetization, you’ll drive revenue growth across our product suite spanning Claude.ai, Claude Code, Claude Cowork, and Claude Platform. You’ll work closely with other growth teams that are focused on driving growth for specific audiences. You’ll also partner with a cross-functional team of engineers, designers, marketers, and data scientists to develop and execute strategies that accelerate our growth while maintaining our commitment to safety and beneficial AI.

Responsibilities:

• Develop and execute a product strategy focused on increasing revenue growth across our suite of products
• Drive monetization efforts spanning the likes of pricing & packaging, free-to-paid conversion, and consumption-based billing
• Analyze product metrics and user feedback to identify monetization opportunities and optimize performance
• Collaborate with engineering, design, and marketing teams to deliver high-impact growth initiatives
• Conduct user research to understand customer needs and pain points
• Define and track key performance indicators (KPIs) for growth initiatives
• Balance rapid iteration with our commitment to AI safety and ethics

Qualifications:

• 6+ years of product management experience, majority in growth focused roles
• Experience working on growth for mass-scale subscription businesses
• Direct experience owning monetization – pricing, packaging, paid conversion, or upgrade funnels
• Strong analytical skills, and experience with A/B testing and funnel optimization
• Excellent communication and stakeholder management skills
• Ability to thrive in a fast-paced, ambiguous environment
• Passion for AI technology and its potential impact on society
• Technical background or ability to work effectively with engineering teams
• Founder experience is a plus

Deadline to apply: None. Applications will be reviewed on a rolling basis. 

About the role:

As a member of the Applied AI team at Anthropic, you will be a technical Product Engineer focused on becoming a trusted technical advisor to Digital Native Businesses - technology companies adopting the Claude API into their core products. You will work closely with customer product and engineering teams as they ship new products powered by Claude: advising on architecture design decisions, developing evaluation frameworks, and guiding customers through the most cutting-edge implementation patterns for LLMs.

Working closely with our Sales, Product, and Engineering teams, you'll guide a focused portfolio of customers from technical discovery through successful deployment. You'll combine deep engineering expertise with customer-facing skills to help customers unlock the full potential of Claude APIs and move their products closer to the frontier, while maintaining our high standards for safety and reliability.

Responsibilities:

• Serve as a specialist technical advisor to Anthropic customers as they deploy new products & workflows with our models: from discovery through deployment, coordinating internally across multiple teams to drive customer success
• Partner with account executives and solutions architects to translate customer business requirements into technical solutions
• Influence technical architecture decisions and customer product strategy by developing customized pilots, prototypes, and evaluation suites
• Lead hands-on technical workshops and code reviews with customer engineering teams
• Identify common design patterns and contribute insights back to our Product and Engineering teams
• Create scalable public and internal assets, documenting the latest LLM prompting, evaling, agentic, and architecture techniques
• Maintain strong knowledge of the latest developments in LLM capabilities, implementation patterns, and AI product development stacks
• Travel occasionally to customer sites for workshops, implementation support, and building relationships
• Attend conferences, lead speaking engagements, write blog posts and white papers on topics surrounding the AI space

You may be a good fit if you have:

• 4+ years of experience in technical roles such as Forward Deployed Engineer, Software Engineer or Technical Product Manager with a desire to work closely with customers. Former technical founders are also encouraged to apply.
• Production experience with LLMs including advanced prompt engineering, agent development and frameworks, evaluation frameworks, transcript analysis, MCP, and deployment at scale
• Strong programming skills with proficiency in Python or TypeScript and experience building production applications
• Ability to navigate ambiguity and execute across domains with intellectual openness, finding simple solutions to complex problems
• High cooperation mindset for cross-organizational collaboration, balancing competing priorities with integrity
• Passion for advancing safe, beneficial AI systems through creative technical applications
• Exceptional communication skills to convey technical concepts to diverse stakeholders while maintaining a low ego and collaborative approach

Please note this role requires 3 days in office per week. 

Deadline to apply: None. Applications will be reviewed on a rolling basis. 

About the design team

Design at Anthropic sits at the intersection of craft, research, and product intuition. We're a small team working on products that millions of people use daily—and on interactions that don't have established conventions yet.

Our work shapes how people experience AI: whether Claude feels like a tool or a collaborator, whether it earns trust or erodes it. We partner closely with engineers and researchers, often designing around capabilities that are emerging in real-time. That means staying close to the models, prototyping rapidly, and being comfortable with ambiguity.

We care deeply about craft—the details that make something feel polished and trustworthy—but we ship fast and learn in the open. We'd rather get something in front of users and iterate than wait for perfection.

Read more here for the type of features we build. 

Responsibilities:

• Contribute to the strategic direction of our tools, rooted in deep user empathy
• Define feature areas with exceptional attention to detail and polish, identifying opportunities to improve quality and consistency of broader flows
• Craft beautiful, polished, and delightful user interfaces that build trust and showcase the power of our AI technology
• Collaborate with product managers, engineers, AI researchers and other stakeholders to define product vision, strategy and roadmaps
• Rapidly prototype ideas using code and other methods to communicate concepts and build excitement
• Find creative ways to ship high-quality work in a fast-paced, often ambiguous, resource-constrained startup environment

Specific projects & responsibilities depend on the team staffing, which we’ll talk about once we’ve seen some more of your work and understood your strengths & interests. A few examples across teams:

• Claude Cowork: Pioneer new ways for Claude to be a true partner in people’s day to day lives that can access your tools, ask for feedback, help you prep for your day, and more; Build expertise in industries like education, financial services, and healthcare to design intuitive experiences that help people leverage AI at work in transformative ways.
• Claude Code: Evolve our industry-leading coding tool, across terminal and Desktop, to deepen the breadth and depth of agentic coding.
• Growth: Design intuitive onboarding flows that help new users get started quickly, understand Claude's capabilities, and achieve their goals in the first session; Uncover barriers that prevent users from getting the most out of Claude and create experiences that empower them to make it a trusted part of their routine.
• Developer Platform: Support frontier capabilities and API features via tooling, workflows, and documentation; Define the future suite of utilities for developers to confidently build, monitor and control agentic systems.
• Consumer: Evolve the Claude mobile app, with a focus on being genuinely helpful for people’s everyday lives: learning, creative projects, personal planning and problem-solving — with an emphasis on quality & safety. 

What we’re looking for:

• You ship fast and stay in motion. You'd rather get something in front of users this week than perfect it for a month. You prototype in code, stay scrappy when speed matters, and know when to polish versus when to learn.
• You're rethinking the basics. Many UI primitives were designed for a different era. You're excited to question fundamental assumptions and invent patterns that feel native to AI.
• You're AI-native in how you work. You're already using Claude Code or similar tools to extend what you can build. You see AI as a creative partner in your own practice.
• You stay close to the models. You pay attention to where capabilities are heading, not just what Claude can do today. You design for the product we're becoming.
• You elevate craft while moving fast. You care about the details—the pixels, the copy, the edge cases—and find ways to maintain that care while shipping quickly.

You may be a good fit if you have:

• Experience designing complex workflows, enterprise/B2B SaaS, developer tools, or API products 
• Strong portfolio showcasing user-centric design thinking, polished UI craftsmanship, and innovative interaction paradigms
• Proven track record of executing end-to-end on large and complex products or a series of products in ambiguous environments
• Excellent collaboration and communication skills to work effectively with cross-functional teams and influence without authority
• Passion for crafting scaled, highly impactful, safe and beneficial artificial intelligence technologies to enable new possibilities
• Experience with prototyping, especially using front-end code (e.g. HTML/CSS/JS) preferred

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 Research 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.

Key Responsibilities:

• Conduct research and implement solutions in areas such as model architecture, algorithms, data processing, and optimizer development
• Independently lead small research projects while collaborating with team members on larger initiatives
• Design, run, and analyze scientific experiments to advance our understanding of large language models
• Optimize and scale our training infrastructure to improve efficiency and reliability
• Develop and improve dev tooling to enhance team productivity
• Contribute to the entire stack, from low-level optimizations to high-level model design

Qualifications:

• Advanced degree (MS or PhD) in Computer Science, Machine Learning, or a related field
• Strong software engineering skills with a proven track record of building complex systems
• Expertise in Python and experience with deep learning frameworks (PyTorch preferred)
• Familiarity with large-scale machine learning, particularly in the context of language models
• Ability to balance research goals with practical engineering constraints
• Strong problem-solving skills and a results-oriented mindset
• Excellent communication skills and ability to work in a collaborative environment
• Care about the societal impacts of your work

Preferred Experience:

• Work on high-performance, large-scale ML systems
• Familiarity with GPUs, Kubernetes, and OS internals
• Experience with language modeling using transformer architectures
• Knowledge of reinforcement learning techniques
• Background in large-scale ETL processes

You'll thrive in this role if you:

• Have significant software engineering experience
• Are results-oriented with a bias towards flexibility and impact
• Willingly take on tasks outside your job description to support the team
• Enjoy pair programming and collaborative work
• Are eager to learn more about machine learning research
• Are enthusiastic to work at an organization that functions as a single, cohesive team pursuing large-scale AI research projects
• Are working to align state of the art models with human values and preferences, understand and interpret deep neural networks, or develop new models to support these areas of research
• View research and engineering as two sides of the same coin, and seek to understand all aspects of our research program as well as possible, to maximize the impact of your insights
• Have ambitious goals for AI safety and general progress in the next few years, and you’re working to create the best outcomes over the long-term.

Sample Projects:

• Optimizing the throughput of novel attention mechanisms
• Comparing compute efficiency of different Transformer variants
• Preparing large-scale datasets for efficient model consumption
• Scaling distributed training jobs to thousands of GPUs
• Designing fault tolerance strategies for our training infrastructure
• Creating interactive visualizations of model internals, such as attention patterns

At Anthropic, we are committed to fostering a diverse and inclusive workplace. We strongly encourage applications from candidates of all backgrounds, including those from underrepresented groups in tech.

If you're excited about pushing the boundaries of AI while prioritizing safety and ethics, we want to hear from you!

About the role

We are looking for a Research Engineer to help us train Claude specifically for virtual collaborator workflows. While Claude excels at general tasks, a lot of knowledge work requires targeted training on real organizational data and workflows. Your job will be to design and implement reinforcement learning (RL) environments that transform Claude into the best virtual collaborator, training on realistic tasks from navigating internal knowledge to creating financial models.

Responsibilities:

• Training Claude on document manipulation with good taste, including understanding, enhancing, and co-creating (e.g., Office doc formats, data visualization)
• Designing and implementing reinforcement learning pipelines targeted at virtual collaborator use cases (productivity, organizational navigation, vertical domains)
• Building and scaling our data creation platform for generating high-quality, open-ended tasks with domain experts and crowdworkers Integrating real organizational data to create realistic training environments
• Developing robust evaluation systems that maintain quality while avoiding reward hacking
• Partnering directly with product teams (e.g., Cowork, claude.ai) to ensure training aligns with product features

You may be a good fit if you:

• Are a very experienced Python programmer who can quickly produce reliable, high quality code that your teammates love using
• Have 5-8 years of strong machine learning experience
• Thrive at the intersection of research and product, with a pragmatic approach to solving real-world problems
• Are comfortable with ambiguity and can balance research rigor with shipping deadlines
• Enjoy collaborating across multiple teams (data operations, model training, product)
• Can context-switch between research problems and product engineering tasks
• Care about making AI genuinely helpful for everyday enterprise workflows

Strong candidates may also have experience with:

• Creating RL envs for realistic tasks.
• Reward modeling and preventing reward hacking
• Building human-in-the-loop training systems or crowdsourcing platforms
• Working with enterprise tools and APIs (Google Workspace, Microsoft Office, Slack, etc.)
• Developing evaluation frameworks for open-ended tasks
• Domain expertise in finance, legal, or healthcare workflows 
• Creating scalable data pipelines with quality control mechanisms
• Translating product requirements into technical training objectives 

Deadline to apply: None. Applications will be reviewed on a rolling basis. 

About the role

Anthropic's production models undergo sophisticated post-training processes to enhance their capabilities, alignment, and safety. As a Research Engineer on our Post-Training team, you'll train our base models through the complete post-training stack to deliver the production Claude models that users interact with.

You'll work at the intersection of cutting-edge research and production engineering, implementing, scaling, and improving post-training techniques like Constitutional AI, RLHF, and other alignment methodologies. Your work will directly impact the quality, safety, and capabilities of our production models.

Note: For this role, we conduct all interviews in Python. This role may require responding to incidents on short-notice, including on weekends.

Responsibilities:

• Implement and optimize post-training techniques at scale on frontier models
• Conduct research to develop and optimize post-training recipes that directly improve production model quality
• Design, build, and run robust, efficient pipelines for model fine-tuning and evaluation
• Develop tools to measure and improve model performance across various dimensions
• Collaborate with research teams to translate emerging techniques into production-ready implementations
• Debug complex issues in training pipelines and model behavior
• Help establish best practices for reliable, reproducible model post-training

You may be a good fit if you:

• Thrive in controlled chaos and are energised, rather than overwhelmed, when juggling multiple urgent priorities
• Adapt quickly to changing priorities
• Maintain clarity when debugging complex, time-sensitive issues
• Have strong software engineering skills with experience building complex ML systems
• Are comfortable working with large-scale distributed systems and high-performance computing
• Have experience with training, fine-tuning, or evaluating large language models
• Can balance research exploration with engineering rigor and operational reliability
• Are adept at analyzing and debugging model training processes
• Enjoy collaborating across research and engineering disciplines
• Can navigate ambiguity and make progress in fast-moving research environments

Strong candidates may also:

• Have experience with LLMs
• Have a keen interest in AI safety and responsible deployment

We welcome candidates at various experience levels, with a preference for senior engineers who have hands-on experience with frontier AI systems. However, proficiency in Python, deep learning frameworks, and distributed computing is required for this role.

About the Team

Our team is organized around the north star goal of building an AI scientist – a system capable of solving the long term reasoning challenges and basic capabilities necessary to push the scientific frontier. Our team likes to think across the whole model stack. Currently the team is focused on improving models' abilities to use computers – as a laboratory for long horizon tasks and a key blocker to many scientific workflows.

About the role

As a Research Engineer on our team you will work end to end, identifying and addressing key blockers on the path to scientific AGI. Strong candidates should have familiarity with language model training, evaluation, and inference, be comfortable triaging research ideas and diagnosing problems and enjoy working collaboratively. Familiarity with performance optimization, distributed systems, vm/sandboxing/container deployment, and large scale data pipelines is highly encouraged.

Join us in our mission to develop advanced AI systems that are both powerful and beneficial for humanity. 

Responsibilities: 

• Working across the full stack to identify and remove bottlenecks preventing progress toward scientific AGI
• Develop approaches to address long-horizon task completion and complex reasoning challenges essential for scientific discovery
• Scaling research ideas from prototype to production
• Create benchmarks and evaluation frameworks to measure model capabilities in scientific workflows and computer use
• Implement distributed training systems and performance optimizations to support large-scale model development

You may be a good fit if you:

• Have 8+ years of ML research experience
• Are familiar with large scale language model training, evaluation, and inference pipelines
• Enjoy obsessively iterating on immediate blockers towards longterm goals
• Thrive working collaboratively to solve problems
• Have expertise in performance optimization and distributed computing systems
• Show strong problem-solving skills and ability to identify technical bottlenecks in complex systems
• Can translate research concepts into scalable engineering solutions
• Have a track record of shipping ML systems that tackle challenging multi-step reasoning problems

Strong candidates may also have:

• Expertise with performance optimization for language model inference and training
• Experience with computer use automation and agentic AI systems
• A history working on reinforcement learning approaches for complex task completion
• Knowledge of containerization technologies (Docker, Kubernetes) and cloud deployment at scale
• Demonstrated ability to work across multiple domains (language modeling, systems engineering, scientific computing)
• Have experience with VM/sandboxing/container deployment and large-scale data processing
• Experience working with large scale data problem solving and infrastructure
• Published research or practical experience in scientific AI applications or long-horizon reasoning

About the role

Anthropic is seeking an exceptional Director of Corporate Accounting to lead our Corporate Accounting organization as we scale our financial operations to support frontier AI development. In this pivotal leadership role, you will own the design, implementation, and continuous improvement of our core accounting infrastructure across workplace, workforce, and corporate operations. You will build and lead a world-class team that ensures accuracy, control, and efficiency while supporting Anthropic's rapid growth.

This role sits at the intersection of technical accounting expertise, systems, and operational excellence. You'll partner closely with Finance & Strategy, Tax, Revenue Accounting, and cross-functional stakeholders to establish scalable processes and controls. As a key leader in the Accounting organization, you will drive strategic initiatives including system selection and implementation, close acceleration, and the buildout of our control framework. Your work will be essential in providing the financial foundation that enables Anthropic to focus on our mission of developing safe, transformative AI systems.

Responsibilities:

Accounting Leadership & Operations

• Own end-to-end accounting for workplace and workforce, including real estate leasing (ASC 842), fixed assets, cash and treasury, operating expenses, prepaids and accruals, payroll, taxes and benefits, commissions, and stock-based compensation support

• Drive month-end and quarter-end close processes, ensuring accuracy, timeliness, and continuous acceleration of close cycles

• Establish and maintain the Chart of Accounts and master data governance structure, ensuring it supports current operations and scales for future complexity

• Design and implement allocation methodologies that provide clear cost visibility across the organization

• Ensure compliance with US GAAP, internal policies, and regulatory requirements across all corporate accounting areas

• Lead process improvement initiatives to increase efficiency, reduce manual work, and enhance data quality

Systems & Infrastructure

• Serve as a key stakeholder in ERP design, selection, and implementation, translating business requirements into system specifications

• Lead selection and implementation of specialized systems for lease accounting and commissions management

• Own master data governance, ensuring data integrity and consistency across financial systems

• Drive adoption of close management tools to streamline workflows and enhance visibility

• Partner with Finance Systems team to optimize integrations and automation opportunities

Team Building & Leadership

• Build, coach, and lead a world-class corporate accounting team capable of supporting Anthropic's growth trajectory

• Recruit top accounting talent with both technical expertise and a growth mindset

• Provide mentorship and career development opportunities, fostering a culture of excellence and continuous learning

• Establish clear roles, responsibilities, and performance expectations as the team scales

• Create an environment that balances rigorous controls with operational agility

Strategic Partnership

• Collaborate with Finance & Strategy to provide accurate data and insights that inform business decisions

• Partner with Tax on compliance requirements and optimization opportunities

• Support global expansion initiatives by establishing accounting processes for new entities and jurisdictions

• Provide technical accounting guidance on complex transactions and new business models

You may be a good fit if you:

• Have 15+ years of progressive accounting experience with at least 5+ years in leadership roles managing teams of 5+ people

• Possess deep technical accounting expertise, particularly in ASC 842 (lease accounting), ASC 718 (stock-based compensation), and complex operational accounting

• Have proven experience building and scaling accounting organizations in high-growth technology companies

• Hold a Bachelor's degree in Accounting or Finance; CPA required

• Are a strategic thinker who can balance technical precision with business pragmatism

• Have successfully led or played a key role in ERP implementations or major systems projects

• Excel at building scalable processes and controls that support rapid growth

• Are comfortable operating in ambiguous environments and building structure where none exists

• Possess exceptional communication skills and can effectively partner with both technical and non-technical stakeholders

• Have a bias toward action and continuous improvement, always asking "how can we do this better?" in a constructive, look forward mentality

• Thrive in fast-paced environments and maintain composure under tight deadlines

• Care about building diverse, inclusive teams and creating environments where people do their best work

Strong candidates may also:

• Have public company accounting experience, including preparation for or execution of IPO readiness activities

• Possess expertise in international accounting, consolidations, and multi-entity operations

• Have experience with NetSuite, Workday, and modern SaaS accounting platforms

• Bring knowledge of subscription and consumption-based revenue models

• Have managed accounting through periods of significant organizational change

• Possess project management skills and experience leading cross-functional initiatives

• Have familiarity with AI/ML business models and infrastructure accounting

• Demonstrate thought leadership through participation in professional organizations or speaking engagements

Deadline to apply: None. Applications will be reviewed on a rolling basis.

About the role:

Anthropic is looking for a talented Technical Recruiter to partner with our Core Tech teams. In this pivotal role, you will be instrumental in shaping the future of our organization by identifying, engaging, and hiring the best and brightest minds across a range of disciplines. As we continue to push the boundaries of AI research and development, we need a passionate recruiter who can help us build a world-class team dedicated to creating safe and beneficial AI systems.

Responsibilities:

• Develop and execute strategic recruiting plans to identify, source, and hire highly qualified candidates, with a focus on Machine Learning and AI talent
• Partner with hiring managers and interviewers to understand hiring needs, team matching, required skills and qualifications
• Enhance and implement recruiting processes and programs while maintaining an inclusive and high talent bar, such as developing targeted outreach campaigns, building connections with industry leaders, and removing any unfair biases from the hiring process
• Collaborate with leadership and cross-functional partners to understand organizational needs and map out long-term talent acquisition strategies that balance priorities across all technical teams
• Enhance Anthropic's employer brand within the research and science community to showcase our mission, culture, and values to candidates
• Stay up-to-date on recruiting best practices, emerging sourcing techniques, interview innovations, and workplace trends

You may be a good fit if you:

• Have 5+ years of experience in full life cycle recruiting supporting core technical teams
• Have a passion for AI's potential to positively impact the world and realistic assessment of its risks and limitations
• Are experimental and are open to new, creative recruiting ideas, or have experience working with hiring managers who are open to non-traditional talent strategies
• Thrive in fast-paced, dynamic environments and enjoy juggling multiple priorities
• Possess strong technical aptitude with the ability to understand and evaluate technical qualifications
• Have enthusiasm for deeply understanding the needs of engineers and innovating on recruiting processes to make them more tailored to the world of AI
• Have excellent organizational skills and attention to detail, as well as a proactive mindset and ability to operate with autonomy
• Have experience partnering with engineers and hiring talent that work on GenAI and LLMs
• Have a proven track record of scaling and building diverse and high-performing teams in a fast-paced, high-growth startup environment

Strong candidates may also:

• Bring a deep interest in AI safety
• Have experience partnering with engineers and hiring talent that work on GenAI and LLMs
• Have experience with academic recruitment and research communities

About the role:

Anthropic’s performance and scaling teams focus on making the most efficient and impactful use of our compute resources, be it inference or training.  As an Engineering Manager on these teams you will be responsible for ensuring you and your team are identifying and removing bottlenecks, building robust and durable solutions, and maximizing the efficiency of our systems.  You also will help bring clarity, focus, and context to your teams in a fast paced, dynamic environment.

Responsibilities:

• Provide front-line leadership of engineering efforts to improve model performance and scale our inference and training systems
• Become familiar with the team’s technical stack enough to make targeted contributions as an individual contributor
• Manage day-to-day execution of the team's work
• Prioritize the team’s work and manage projects in a highly dynamic, fast paced environment
• Coach and support your reports in understanding, and pursuing, their professional growth
• Maintain a deep understanding of the team's technical work and its implications for AI safety

You may be a good fit if you:

• Have 1+ years of management experience in a technical environment, particularly performance or distributed systems
• Have a background in machine learning, AI, or a similar related technical field
• Are deeply interested in the potential transformative effects of advanced AI systems and are committed to ensuring their safe development
• Excel at building strong relationships with stakeholders at all levels
• Are a quick learner, capable of understanding and contributing to discussions on complex technical topics
• Have experience managing teams through periods of rapid growth and change
• Are a quick study: this team sits at the intersection of a large number of different complex technical systems that you’ll need to understand (at a high level of abstraction) to be effective

Strong candidates may also have experience with: 

• High performance, large-scale ML systems
• GPU/Accelerator programming
• ML framework internals
• OS internals
• Language modeling with transformers

About the Role

We're seeking a Platform Hardware Security Engineer to design and implement security architectures for bare-metal infrastructure. You'll work with teams across Anthropic to build firmware, bootloaders, operating systems, and attestation systems to ensure the integrity of our infrastructure from the ground up.

This role requires expertise in low-level systems security and the ability to architect solutions that balance security requirements with the performance demands of training AI models across our massive fleet.

What you'll do:

• Design and implement secure boot chains from firmware through OS initialization for diverse hardware platforms (CPUs, BMCs, switches, peripherals, and embedded microcontrollers)
• Architect attestation systems that provide cryptographic proof of system state from hardware root of trust through application layer
• Develop measured boot implementations and runtime integrity monitoring
• Create reference architectures and security requirements for bare-metal deployments
• Integrate security controls with infrastructure teams without impacting training performance
• Prototype and validate security mechanisms before production deployment
• Conduct firmware vulnerability assessments and penetration testing
• Build firmware analysis pipelines for continuous security monitoring
• Document security architectures and maintain threat models
• Collaborate with software and hardware vendors to ensure security capabilities meet our requirements

Who you are:

• 8+ years of experience in systems security, with at least 5 years focused on firmware and hardware security (firmware, bootloaders, and OS-level security)
• Hands-on experience with secure boot, measured boot, and attestation technologies (TPM, Intel TXT, AMD SEV, ARM TrustZone)
• Strong understanding of cryptographic protocols and hardware security modules
• Experience with UEFI/BIOS or embedded firmware security, bootloader hardening, and chain of trust implementation
• Proficiency in low-level programming (C, Rust, Assembly) and systems programming
• Knowledge of firmware vulnerability assessment and threat modeling
• Track record of designing security architectures for complex, distributed systems
• Experience with supply chain security
• Ability to work effectively across hardware and software boundaries
• Knowledge of NIST firmware security guidelines and hardware security frameworks

Strong candidates may also have:

• Experience with confidential computing technologies and hardware-based TEEs
• Knowledge of SLSA framework and software supply chain security standards
• Experience securing large-scale HPC or cloud infrastructure
• Contributions to open-source security projects (coreboot, CHIPSEC, etc.)
• Background in formal verification or security proof techniques
• Experience with silicon root of trust implementations
• Experience working with building foundational technical designs, operational leadership, and vendor collaboration
• Previous work with AI/ML infrastructure security

Deadline to apply: None. Applications will be reviewed on a rolling basis. 

About the role:

We are seeking an experienced People Legal Specialist to join our growing legal team and serve as the operational backbone for a small, high-impact team that advises on how Anthropic interacts with the people who power our mission: employees, candidates, advisors, and contingent workers globally. You will be a trusted project manager and process-builder, working under the guidance of legal counsel to keep employment matters running efficiently so our attorneys can focus on the thorniest strategic questions. You will partner closely with colleagues on the Litigation & Regulatory team and People team operations functions to help the People Legal team scale its support for a rapidly growing, globally distributed workforce at the frontier of AI.

Responsibilities: 

• Monitor incoming claims, demands, and agency inquiries to ensure Anthropic responds in a timely and appropriate manner; maintain matter-tracking systems and keep stakeholders informed of status
• Act as the primary operational link between People Legal and partner teams to ensure context-rich handoffs and integrated, efficient delivery of legal support
• Maintain accurate records and documentation related to legal matters, including managing case files and calendars
• Handle personnel document requests, ensuring compliance with relevant regulations and timely responses to requests 
• Support attorneys with legal research, document preparation, and template management; help distill work product into clear, actionable formats for internal stakeholders
• Identify opportunities to streamline processes and leverage tools—including Claude—to reduce repetitive work and free up team bandwidth for higher-value contributions
• Flex beyond core responsibilities as the team's needs evolve, pitching in on special projects and cross-functional initiatives as they arise

You may be a good fit if you have:

• At least 8 years of relevant experience in legal operations or paralegal role in a corporate legal department or similar experience.
• Excellent communication skills, both verbal and written, with the ability to effectively interact with individuals at all levels of the organization.
• Demonstrated the ability to exercise good judgment and maintain the highest standards of discretion and confidentiality when handling sensitive legal matters.
• Strong project management skills, including the ability to prioritize tasks, meet deadlines, and manage multiple projects simultaneously.
• Proactive and solutions-oriented approach to identifying, recommending, and implementing process improvements within the legal department.
• Comfort building processes from scratch, not just executing on established playbooks

Strong candidates may have:

• Comfort experimenting with AI tools to make legal operations more efficient, or a genuine curiosity about learning to do so
• 2+ years of experience working in a high-growth tech company, with exposure to US and international employment matters
• Demonstrated the ability to think critically and creatively problem-solve, adapting to new challenges and finding innovative solutions to complex legal issues.
• Familiarity with legal project management software, such as Asana, Trello, or Jira, and a willingness to learn and adopt new technologies to streamline legal processes.
• Experience using legal research databases (PACER, LexisNexis, Pohlman, Westlaw)

Role-specific policy: For this role, we expect all staff to be able to work from our San Francisco, Seattle, or New York offices at least 3 days a week, though we encourage you to apply even if you might need some flexibility for an interim period of time.

About the Role

As a TPU Kernel Engineer, you'll be responsible for identifying and addressing performance issues across many different ML systems, including research, training, and inference. A significant portion of this work will involve designing and optimizing kernels for the TPU. You will also provide feedback to researchers about how model changes impact performance. Strong candidates will have a track record of solving large-scale systems problems and low-level optimization.

You may be a good fit if you:

• Have significant experience optimizing ML systems for TPUs, GPUs, or other accelerators
• Are results-oriented, with a bias towards flexibility and impact
• Pick up slack, even if it goes outside your job description
• Enjoy pair programming (we love to pair!)
• Want to learn more about machine learning research
• Care about the societal impacts of your work

Strong candidates may also have experience with: 

• High performance, large-scale ML systems
• Designing and implementing kernels for TPUs or other ML accelerators
• Understanding accelerators at a deep level, e.g. a background in computer architecture
• ML framework internals
• Language modeling with transformers

Representative projects:

• Implement low-latency, high-throughput sampling for large language models
• Adapt existing models for low-precision inference
• Build quantitative models of system performance
• Design and implement custom collective communication algorithms
• Debug kernel performance at the assembly level

About the role

We are seeking an experienced Engineering Manager to lead the Cloud Inference team for AWS. You will lead your team to scale and optimize Claude to serve the massive audiences of developers and enterprise companies using AWS. You will own the end-to-end product of Claude on AWS, including API, load balancing, inference, capacity and operations. Your team will ensure our LLMs meet rigorous performance, safety and security standards and enhance our core infrastructure for packaging, testing, and deploying inference technology across the globe. Your work will increase the scale at which Anthropic operates and accelerate our ability to reliably launch new frontier models and innovative features to customers across all platforms.

Responsibilities:

• Set technical strategy and oversee development of Claude on AWS across all layers of the technical stack.
• Collaborate across teams and companies to deeply understand product, infrastructure, operations and capacity needs, identifying potential solutions to support frontier LLM serving
• Work closely with cross-functional stakeholders across companies to align on goals and drive outcomes
• Create clarity for the team and stakeholders in an ambiguous and evolving environment
• Take an inclusive approach to hiring and coaching top technical talent, and support a high performing team
• Design and run processes (e.g. postmortem review, incident response, on-call rotations) that help the team operate effectively and never fail the same way twice

You may be a good fit if you:

• Have 10+ years of experience in high-scale, high-reliability software development, particularly infrastructure or capacity management
• Have 5+ years of engineering management experience
• Experience recruiting, scaling, and retaining engineering talent in a high growth environment
• Have experience scaling products, resources and operations to accommodate rapid growth
• Are deeply interested in the potential transformative effects of advanced AI systems and are committed to ensuring their safe development
• Excel at building strong relationships and strategy with stakeholders across engineering, product, finance, and sales
• Have experience working with external partners to align goals and deliver impact
• Enjoy working in a fast-paced, early environment; comfortable with adapting priorities as driven by the rapidly evolving AI space
• Have excellent written and verbal communication skills
• Demonstrated success building a culture of belonging and engineering excellence
• Are motivated by developing AI responsibly and safely
• Are willing and able to travel frequently between Seattle and the SF Bay Area

Strong candidates may also have experience with:

• Experience with machine learning infrastructure like GPUs, TPUs, or Trainium, as well as supporting networking infrastructure like NCCL
• Experience as a Product Manager
• Experience with deployment and capacity management automation
• Security and privacy best practice expertise

Deadline to apply: None. Applications will be reviewed on a rolling basis. 

About the role

Join Anthropic's Customer Success team in a high-visibility & high-impact role driving AI adoption across our strategic Digital Native Business (DNB) accounts. As our dedicated Enterprise Customer Success Manager for a global technology leader with 100k employees' you’ll be their strategic partner and trusted advisor helping them harness the full potential of all our Claude capabilities - API, Claude for Enterprise, and Claude Code.

You'll work with a global technology leader with 100k employees’ to actively deploy AI to reshape the technology landscape.You’ll be working with key partners who are moving fast and pushing the boundaries of what's possible with LLM technology. Your role will entail developing genuine partnerships with the customer and key stakeholders, gaining a deep understanding of their multi-pronged business objectives, strategic direction, AI vision, and technical needs. You'll draw on both your business acumen and technical expertise to serve as a strategic advisor throughout their journey with us. 

In partnership with the broader account team, you will help customers identify the right Claude capabilities for their specific business objectives, working closely with them to provide best practices and guidance while supporting them as their usage (consumption & seat based) grows and evolves.

Your role focuses on helping customers scale their usage effectively, drive model and use case optimizations, implement change management strategies, and maximize the value of their investment through expanded use cases across their organization.  The insights you gather from your customers will directly inform our research priorities, product development, and go-to-market strategies — making you a key voice in shaping how we build and deliver ongoing value as a business.

Responsibilities:

• Build trusting, strategic relationships with key customer decision makers in complex, matrixed organizations; understand their business and objectives and identify opportunities for optimization and expansion
• Become an expert in Anthropic's products across API, Claude Code and Claude for Enterprise, understanding the technical nuances and best practices for each to guide customers to the right solutions
• Leverage your deep knowledge of the customer and other Digital Native Businesses  to proactively drive usage planning, understanding current and future consumption/ adoption and how it creates realized value for the customer
• Monitor usage patterns and identify optimization opportunities, proactively addressing underutilization across both consumption-based (API) and seat-based (Claude for Enterprise / Claude Code) products to drive full value from contracted commitments
• Serve as the customer’s thought partner, enhancing their knowledge of Claude products by socializing Anthropic’s product roadmap, driving awareness on new products and engaging Product PMs
• Document and quantify customer value realized through business outcomes, ROI, and impact metrics to build compelling internal business cases for continued and expanded investment
• Identify potential use cases and lines of business not currently onboarded, partnering with customers and Sales/Product to discover new applications for Claude across different departments, teams, and workflows
• Develop and execute change management strategies to drive end-user adoption and maximize value within customer organizations, including Train the Trainer programs, Center of Excellence development, and organizational enablement
• Own the customer experience across their lifecycle — managing comprehensive account and success plans grounded in the customer's business objectives, conducting Quarterly Business Reviews, and serving as the primary conduit between the customer and Anthropic
• Develop scalable engagement strategies and playbooks for your customer that can be utilized across other high-touch strategic DNB accounts to maximize impact across all customers

You may be a good fit if you have:

• 8+ years of experience in Customer Success, Technical Account Management, or Solutions Engineering
• Experience working with both F10 and F500 technology companies, SaaS platforms, or digital-first businesses—ideally including high-growth and established tech companies
• Deep understanding of the AI landscape, including direct experience working for or with large technology companies with investments/products at every layer of the AI tech stack
• Technical fluency with ability to understand and articulate AI/ML concepts, API integrations, and software implementation patterns across a set of diverse stakeholders—from developers and product managers to executives and end users
• Experience driving success across both consumption-based and seat-based business models, with understanding of different expansion levers and success metrics for each
• Strategic mindset to identify growth opportunities and translate them into actionable expansion plans
• Proven track record managing a portfolio of accounts while maintaining strong relationships and driving measurable outcomes
• Cross-functional collaborator who represents the customer in a positive, proactive manner, rallying everyone around paths forward that solve customer needs
• Passion for AI and interest in responsible development of advanced systems
• A knack for bringing order to chaos and an enthusiastic "roll up your sleeves" mentality—you're a true team player and view yourself as the COO of your customer accounts

At Anthropic, we are building some of the most complex and large-scale AI infrastructure in the world. As that infrastructure scales rapidly, so does the imperative to optimize how we use it. As a Software Engineer for Compute Efficiency on the Capacity team, you will play a central role in making our systems more performant, cost-effective, and sustainable—without compromising reliability or latency.

You will work across the full infrastructure stack, from cloud platforms and networking to application-level performance, and will bridge the gap between high-level research needs and low-level hardware constraints to build the most efficient AI infrastructure in the world. You will help with building the telemetry, cost attribution, and optimization frameworks that ensure every dollar of our infrastructure investment delivers maximum value. This is a high-impact, cross-functional role at the intersection of systems engineering, financial optimization, and AI infrastructure.

Responsibilities:

• Build and evolve telemetry and monitoring systems to provide deep visibility into infrastructure performance, utilization, and costs across our cloud and datacenter fleets.

• Design and implement cost attribution frameworks for our multi-tenant infrastructure, enabling teams to understand and optimize their resource consumption.

• Identify and resolve performance bottlenecks and capacity hotspots through deep analysis of distributed systems at scale.

• Partner closely with cloud service providers and internal stakeholders to optimize cluster configurations, workload placement, and resource utilization across AI training and inference workloads—including large-scale clusters spanning thousands to hundreds of thousands of machines.

• Develop and champion engineering practices around efficiency, driving a culture of performance awareness and cost-conscious design across Anthropic.

• Collaborate with research and product teams to deeply understand their infrastructure needs, and design solutions that balance performance with cost efficiency.

• Drive architectural improvements and code-level optimizations across multiple services and platforms to deliver measurable utilization and performance gains.

You may be a good fit if you:

• Have 6+ years of relevant industry experience, 1+ year leading large scale, complex projects or teams as a software engineer or tech lead

• Deep expertise in distributed systems at scale, with a strong focus on infrastructure reliability, scalability, and continuous improvement.

• Strong proficiency in at least one programming language (e.g., Python, Rust, Go, Java)

• Hands-on experience with cloud infrastructure, including Kubernetes, Infrastructure as Code, and major cloud providers such as AWS or GCP.

• Experience optimizing end-to-end performance of distributed systems, including workload right-sizing and resource utilization tuning.

• You possess a deep curiosity for how things work under the hood and have a proven ability to work independently to solve opaque performance issues

• Experience designing or working with performance and utilization monitoring tools in large-scale, distributed environments.

• Strong problem-solving skills with the ability to work independently and navigate ambiguity.

• Excellent communication and collaboration skills—you will work closely with internal and external stakeholders to build consensus and drive projects forward.

Strong candidates may have:

• Experience with machine learning infrastructure workloads as well as associated networking technologies like NCCL.

• Low level systems experience, for example linux kernel tuning and eBPF 

• Quickly understanding systems design tradeoffs, keeping track of rapidly evolving software systems

• Published work in performance optimization and scaling distributed systems

About the role:

Anthropic’s performance and scaling teams focus on making the most efficient and impactful use of our compute resources, be it inference or training.  As an Engineering Manager on these teams you will be responsible for ensuring you and your team are identifying and removing bottlenecks, building robust and durable solutions, and maximizing the efficiency of our systems.  You also will help bring clarity, focus, and context to your teams in a fast paced, dynamic environment.

Responsibilities:

• Provide front-line leadership of engineering efforts to improve model performance and scale our inference and training systems
• Become familiar with the team’s technical stack enough to make targeted contributions as an individual contributor
• Manage day-to-day execution of the team's work
• Prioritize the team’s work and manage projects in a highly dynamic, fast paced environment
• Coach and support your reports in understanding, and pursuing, their professional growth
• Maintain a deep understanding of the team's technical work and its implications for AI safety

You may be a good fit if you:

• Have 1+ years of management experience in a technical environment, particularly performance or distributed systems
• Have a background in machine learning, AI, or a similar related technical field
• Are deeply interested in the potential transformative effects of advanced AI systems and are committed to ensuring their safe development
• Excel at building strong relationships with stakeholders at all levels
• Are a quick learner, capable of understanding and contributing to discussions on complex technical topics
• Have experience managing teams through periods of rapid growth and change
• Are a quick study: this team sits at the intersection of a large number of different complex technical systems that you’ll need to understand (at a high level of abstraction) to be effective

Strong candidates may also have experience with: 

• High performance, large-scale ML systems
• GPU/Accelerator programming
• ML framework internals
• OS internals
• Language modeling with transformers

About the role

As a Product Manager for Claude Code, you will drive the evolution of the Claude Code product suite and help shape how developers work with AI. You will work closely with internal users and customers to prioritize their requirements and ship improvements to Claude Code. You will work closely with the engineering team, research team, and XFN teams to ensure Claude Code remains ahead of model capabilities and is seen as the best way to experience the most intelligent Claude models.

In just a few months since launch, Claude Code has already redefined how developers interact with AI. Yet we are only scratching the surface. As model intelligence accelerates, the ultimate form factor of agentic software development remains unwritten. We seek a product manager who combines deep technical understanding, genuine love for developers, and strong product intuition. You should be equally comfortable discussing customer feedback with world-class researchers, debugging user workflows with engineers, and presenting product strategy to executives.

Responsibilities:

• Define the roadmap for an area of the Claude Code product suite
• Translate cutting-edge AI advances into practical developer features
• Build an ecosystem around the CLI and other products so that developers can easily share best practices

You might be a good fit if you:

• Have a combined 5+ years in product management and engineering, including at least 1 year as a professional engineer. 
• Have a deep technical background with experience working cross-functionally with engineering teams to ship technical products.
• Track record of launching ambitious products that have achieved distribution or commercial success. Experience in dev tools is preferred.
• Stay up-to-date and hands-on with AI coding tools, model capabilities, and industry trends
• Have a creative, hacker spirit and love solving puzzles

You may be a good fit if you:

• Have significant software engineering or machine learning experience, particularly at supercomputing scale
• Are results-oriented, with a bias towards flexibility and impact
• Pick up slack, even if it goes outside your job description
• Enjoy pair programming (we love to pair!)
• Want to learn more about machine learning research
• Care about the societal impacts of your work

Strong candidates may also have experience with: 

• High performance, large-scale ML systems
• GPU/Accelerator programming
• ML framework internals
• OS internals
• Language modeling with transformers

Representative projects:

• Implement low-latency high-throughput sampling for large language models
• Implement GPU kernels to adapt our models to low-precision inference
• Write a custom load-balancing algorithm to optimize serving efficiency
• Build quantitative models of system performance
• Design and implement a fault-tolerant distributed system running with a complex network topology
• Debug kernel-level network latency spikes in a containerized environment

Deadline to apply: None. Applications will be reviewed on a rolling basis.