You'll shape controls, architecture, and tooling so that security is part of how the platform works by default. You'll partner closely with research and product teams, enabling them to move quickly while keeping our models, data, and environments protected.

Key responsibilities include:

Architecting security patterns for platforms and services, including network segmentation, service-to-service authentication, RBAC, and policy enforcement in Kubernetes and cloud environments.

Managing identity, access, and secrets for humans and services: workload and cross-cloud identity, least-privilege IAM, and secrets management.

Building secure platforms for data ingestion, processing, and curation: classification, encryption, access controls, and safe sharing patterns across teams.

Writing threat models and reviewing designs with researchers and engineers to help them ship features and experiments in a safe, scalable way.

Automating security checks and building guardrails: policy-as-code, secure infrastructure baselines, validation in CI/CD, and tools that make the secure path the easiest one.

Requirements include:

Bachelor's degree or equivalent experience in engineering, or similar.

Strong background with containers and orchestration (e.g., Kubernetes) and how to secure them (namespaces, network policies, pod security, admission controls, etc.).

Practical experience with Infrastructure as Code (Terraform or similar), including secure patterns for provisioning networks, IAM, and shared services.

Solid understanding of cloud networking and security: VPCs, load balancers, service discovery, mTLS, firewalls, and zero-trust-style architectures.

Proficiency with a systems language such as Rust and scripting in Python for building platform components and internal tools.

Evidence of owning complex, production-critical systems, including debugging issues that span infra, security, and application layers.

Preferred qualifications include experience with ML infrastructure, GPU clusters, or large-scale training environments, as well as background in AI labs, HPC environments, or ML-heavy organizations.

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This role blends research and engineering responsibilities, requiring you to both implement novel approaches and contribute to research direction. You'll work on fundamental research in reinforcement learning, designing training environments and methodologies that push the state of the art, and building evaluations that measure genuine capability.

Responsibilities:

• Build the next generation of agentic environments
• Build rigorous evaluations that measure real capability
• Collaborate across research and infrastructure teams to ship environments into production training
• Debug and iterate rapidly across research and production ML stacks
• Contribute to research culture through technical discussions and collaborative problem-solving

You may be a good fit if you:

• Are highly impact-driven , you care about outcomes, not activity
• Operate with high agency
• Have good research taste or senior technical experience, demonstrating good judgment in identifying what actually matters in complex problem spaces
• Can balance research exploration with engineering implementation
• Are passionate about the potential impact of AI and are committed to developing safe and beneficial systems
• Are comfortable with uncertainty and adapt quickly as the landscape shifts
• Have strong software engineering skills and can build robust infrastructure
• Enjoy pair programming (we love to pair!)

Strong candidates may also have one or more of the following:

• Have industry experience with large language model training, fine-tuning or evaluation
• Have industry experience building RL environments, simulation systems, or large-scale ML infrastructure
• Senior experience in a relevant technical field even if transitioning domains
• Deep expertise in sandboxing, containerization, VM infrastructure, or distributed systems
• Published influential work in relevant ML areas

The annual compensation range for this role is $500,000-$850,000 USD.

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The Training Technician works closely with training leadership, instructional designers, mission operations trainers, and cross-functional engineering and operations teams. This is a hands-on, execution-focused role that enables high-quality delivery of live, hybrid, and customer-facing training without owning training content strategy. It bridges technical readiness with training execution.

Key responsibilities include setting up, configuring, and maintaining training hardware, software, and diagnostic tools prior to training events, ensuring training systems and simulators reflect current configurations compliant with production standards and product releases, testing and validating functionality of equipment used in live and practical training environments, and troubleshooting technical issues quickly to minimize disruptions during training sessions.

Additionally, the Training Technician will provide on-site technical support for instructors and trainees during classroom, workshop, and hands-on training events, operate and support AV technology, connectivity, virtual-classroom tools, and LMS tools during live and hybrid sessions, assist with practical setups for simulations, demonstrations, and equipment exercises, and serve as first-line technical contact for training participants experiencing technical issues.

Required qualifications include a technical associate or bachelor's degree in Information Technology, Engineering Technology, or a related technical field, demonstrated experience supporting advanced technical systems in a hands-on environment, strong troubleshooting and problem-solving skills with hardware, software, and networked systems, and excellent communication skills and ability to work with technical and non-technical audiences.

Preferred qualifications include experience in support of live training environments, mission operations, simulation labs, or field training, familiarity with autonomous systems, maritime systems, or complex hardware configurations, prior experience with LMS tools, AV support equipment, and training delivery support, and experience with DoD, defense contractor, or regulated industry environments.

The Training Technician will work in a dynamic training environment with rapid setup and change, and will be required to adapt to shifting priorities and dynamic training schedules. The role will also involve prolonged periods of sitting at a desk and working on a computer, manual dexterity to operate a computer keyboard, mouse, and other office equipment, lifting and carrying items up to 20 pounds occasionally, and ability to work in varying environmental conditions including shipyards, fabrication spaces, and onboard vessels.

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We're looking for Research Engineers to help us build the next generation of training environments for capable and safe agentic AI. This role blends research and engineering responsibilities, requiring you to both implement novel approaches and contribute to research direction.

Responsibilities:

• Build the next generation of agentic environments
• Build rigorous evaluations that measure real capability
• Collaborate across research and infrastructure teams to ship environments into production training
• Debug and iterate rapidly across research and production ML stacks
• Contribute to research culture through technical discussions and collaborative problem-solving

You may be a good fit if you:

• Are highly impact-driven — you care about outcomes, not activity
• Operate with high agency
• Have good research taste or senior technical experience, demonstrating good judgment in identifying what actually matters in complex problem spaces
• Can balance research exploration with engineering implementation
• Are passionate about the potential impact of AI and are committed to developing safe and beneficial systems
• Are comfortable with uncertainty and adapt quickly as the landscape shifts
• Have strong software engineering skills and can build robust infrastructure
• Enjoy pair programming (we love to pair!)

Strong candidates may also have one or more of the following:

• Have industry experience with large language model training, fine-tuning or evaluation
• Have industry experience building RL environments, simulation systems, or large-scale ML infrastructure
• Senior experience in a relevant technical field even if transitioning domains
• Deep expertise in sandboxing, containerization, VM infrastructure, or distributed systems
• Published influential work in relevant ML areas

Logistics

• Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience.
• Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.
• Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

How we're different

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.

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Location

San Francisco; New York City

Employment Type

Full time

Department

Scaling

Compensation

• $230K – $385K • Offers Equity

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.

Benefits

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts

• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)

• 401(k) retirement plan with employer match

• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)

• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees

• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)

• Mental health and wellness support

• Employer-paid basic life and disability coverage

• Annual learning and development stipend to fuel your professional growth

• Daily meals in our offices, and meal delivery credits as eligible

• Relocation support for eligible employees

• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

About the Team

The Agent Infrastructure team at OpenAI is responsible for building systems that enable training and deployment of highly useful AI agents, both internally and for the world.

We work hand-in-hand with researchers to design and scale the environment in which agentic models are trained – providing a workspace for AI models to execute code, debug issues, and develop software just as human SWEs do. Our training environment for agentic models operates at an extremely high scale and has the flexibility to emulate any environment in which an agent might work.

At the same time, our team builds and maintains OpenAI’s core platform for the deployment and execution of agents in production. Our systems power products such as Codex, Operator, tool use in ChatGPT, and future agentic products.

About the Role

As a Software Engineer on the Agent Infrastructure team, you will have the opportunity to work closely with both research and product at OpenAI - building and scaling systems to train highly capable agentic models, and building the platform and integrations to launch new agents to hundreds of millions of users worldwide.

Your work will consist of both building new capabilities - standing up the infrastructure and integrations needed to train more complex agentic models - and rapidly scaling these new capabilities to some of the largest compute clusters in the world. At the same time, you’ll be instrumental to the launch of agentic products at OpenAI - building, maintaining, and scaling the production platform on which all agents run.

Responsibilities

• Push massive compute clusters to their limits. You will be a core contributor to a novel container orchestration platform built in-house by our team to scale far beyond what’s possible with systems like Kubernetes.

• Develop and maintain FastAPI and gRPC APIs that serve as the interface for our agentic infrastructure used both in training and production.

• Use Terraform to stand up and evolve complex infrastructure for both research and production.

• Collaborate with research teams to stand up and optimize systems for novel AI training runs and experimental applications.

Requirements

• Have deep experience working on large-scale machine learning infrastructure. You know how to reason about training at scale, identifying bottlenecks and engineering solutions to optimize system performance in training environments.

• Know how to build new things from 0-1 quickly, and then scale them 1,000,000x.

• Have a keen eye for performance and optimization. You know how to squeeze the most performance out of complex, globally-distributed systems.

• Know your way around cloud platforms and work with infrastructure-as-code tech like Terraform.

• Are driven by solving complex, ambiguous problems at the intersection of infrastructure scalability, virtualization efficiency, and agentic capabilities.

• Have deep technical expertise in virtualization and containerization technologies (e.g. Kata, Firecracker, gVisor, Sysbox) and are passionate about optimizing runtime performance.

What We Offer

• Competitive salary and equity package

• Opportunity to work on cutting-edge AI infrastructure

• Collaborative and dynamic team environment

• Flexible work arrangements

• Professional development opportunities

• Access to the latest technology and tools

How to Apply

If you are a motivated and experienced software engineer looking to join a dynamic team and work on cutting-edge AI infrastructure, please submit your application. We look forward to hearing from you!

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