As a hands-on contributor to the core systems that ensure the safety, reliability, and continuous improvement of LLM-powered workflows and agents for the enterprise, you will partner with Scale's Operations team and enterprise customers to translate ambiguity into structured evaluation data. This involves guiding the creation and maintenance of gold-standard human-rated datasets and expert rubrics that anchor AI evaluation systems.

Your responsibilities will also include analysing feedback and collected data to identify patterns, refine evaluation frameworks, and establish iterative improvement loops that enhance the quality and relevance of human-curated assessments. You will design, research, and develop LLM-as-a-Judge autorater frameworks and AI-assisted evaluation systems, including creating models that critique, grade, and explain agent outputs.

To succeed in this role, you will need a strong foundational knowledge of large language models, a passion for tackling complex evaluation challenges, and the ability to thrive in a dynamic, fast-paced research environment. You should be able to think outside the box, stay current with the latest literature in AI evaluation, and be passionate about integrating novel research ideas into our workflows to build best-in-class evaluation systems.

In addition to your technical expertise, you will need excellent communication and collaboration skills, as you will work closely with cross-functional teams to drive project success.

If you are a motivated and detail-oriented individual with a passion for AI research and evaluation, we encourage you to apply for this exciting opportunity.

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About Anthropic

Anthropic's mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole.

About the Role

Safeguards Engineering builds and operates the infrastructure that keeps Anthropic's AI systems safe in production , the classifiers, detection pipelines, evaluation platforms, and monitoring systems that sit between our models and the real world. That infrastructure needs to be not just correct, but reliable: when a safety-critical pipeline goes down or degrades, the consequences can be serious, and they can be invisible until someone looks closely.

As a Technical Program Manager for Safeguards Infrastructure and Evals, you'll own the operational health and forward momentum of this stack. Your primary responsibility is driving reliability , owning the incident-response and post-mortem process, ensuring SLOs are defined and met in partnership with various teams, and making sure that when things go wrong, the right people know, the right actions get taken, and those actions actually get closed out.

Alongside that ongoing operational rhythm, you'll coordinate the larger platform investments: migrations, eval-platform improvements, and the cross-team dependencies that connect them. This role sits at the intersection of operations and program management. It requires genuine technical depth , you need to understand how these systems work well enough to triage effectively, judge what's actually safety-critical versus what can wait, and have informed conversations with the engineers building and maintaining them. But the core of the job is keeping the machine running well and the work moving.

What You'll Do:

• Own the Safeguards Engineering ops review
• Drive the recurring cadence that keeps the team informed and coordinated: surfacing recent incidents and failures, bringing visibility to reliability trends, and making sure the right people are in the room when decisions need to be made.
• Drive incident tracking and post-mortem execution
• Establish and maintain SLOs with partner teams
• Maintain runbook quality and incident-ownership clarity
• Drive platform migrations and infrastructure projects
• Coordinate evals platform improvements

You might be a good fit if you:

• Have solid technical program management experience, particularly in operational or infrastructure-heavy environments , you're comfortable owning a mix of ongoing operational cadences and discrete project work simultaneously.
• Understand how production ML systems work well enough to triage incidents intelligently and have substantive conversations with engineers about what's going wrong and why , you don't need to write the code, but you need to follow the technical thread.
• Are energized by closing loops. Post-mortem action items that never get done, SLOs that no one checks, runbooks that go stale , these things bother you, and you know how to build the processes and follow-ups that fix them.
• Can work effectively across team boundaries , comfortable coordinating with partner teams (like Inference) where you don't have direct authority, and skilled at keeping shared work moving through influence and clear communication.
• Thrive in environments where the work shifts between 'keep the lights on' and 'build something new' , and can context-switch between incident follow-ups and longer-horizon platform projects without dropping either.
• Have experience with or strong interest in AI safety , you understand why the reliability of a safety-critical pipeline is a different kind of problem than the reliability of a product feature, and that distinction motivates you.

Strong candidates may also:

• Have experience with SRE practices, incident management frameworks, or on-call operations at scale.
• Have worked on or with evaluation infrastructure for ML systems , understanding how evals get designed, run, and interpreted.
• Have experience driving infrastructure migrations in complex, multi-team environments , particularly where the migration touches operational systems that can't go offline.
• Be familiar with monitoring and alerting tooling (PagerDuty, Datadog, or equivalents) and the operational culture around them.

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

The annual compensation range for this role is listed below. For sales roles, the range provided is the role's On Target Earnings ('OTE') range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.

Annual Salary: $290,000-$365,000 USD

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Anthropic's mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

About the Role:

Anthropic's ML Performance and Scaling team trains our production pretrained models, work that directly shapes the company's future and our mission to build safe, beneficial AI systems. As a Research Engineer on this team, you'll ensure our frontier models train reliably, efficiently, and at scale. This is demanding, high-impact work that requires both deep technical expertise and a genuine passion for the craft of large-scale ML systems.

This role lives at the boundary between research and engineering. You'll work across our entire production training stack: performance optimisation, hardware debugging, experimental design, and launch coordination. During launches, the team works in tight lockstep, responding to production issues that can't wait for tomorrow.

Responsibilities:

• Own critical aspects of our production pretraining pipeline, including model operations, performance optimisation, observability, and reliability
• Debug and resolve complex issues across the full stack—from hardware errors and networking to training dynamics and evaluation infrastructure
• Design and run experiments to improve training efficiency, reduce step time, increase uptime, and enhance model performance
• Respond to on-call incidents during model launches, diagnosing problems quickly and coordinating solutions across teams
• Build and maintain production logging, monitoring dashboards, and evaluation infrastructure
• Add new capabilities to the training codebase, such as long context support or novel architectures
• Collaborate closely with teammates across SF and London, as well as with Tokens, Architectures, and Systems teams
• Contribute to the team's institutional knowledge by documenting systems, debugging approaches, and lessons learned

You May Be a Good Fit If You:

• Have hands-on experience training large language models, or deep expertise with JAX, TPU, PyTorch, or large-scale distributed systems
• Genuinely enjoy both research and engineering work—you'd describe your ideal split as roughly 50/50 rather than heavily weighted toward one or the other
• Are excited about being on-call for production systems, working long days during launches, and solving hard problems under pressure
• Thrive when working on whatever is most impactful, even if that changes day-to-day based on what the production model needs
• Excel at debugging complex, ambiguous problems across multiple layers of the stack
• Communicate clearly and collaborate effectively, especially when coordinating across time zones or during high-stress incidents
• Are passionate about the work itself and want to refine your craft as a research engineer
• Care about the societal impacts of AI and responsible scaling

Strong Candidates May Also Have:

• Previous experience training LLM’s or working extensively with JAX/TPU, PyTorch, or other ML frameworks at scale
• Contributed to open-source LLM frameworks (e.g., open\_lm, llm-foundry, mesh-transformer-jax)
• Published research on model training, scaling laws, or ML systems
• Experience with production ML systems, observability tools, or evaluation infrastructure
• Background as a systems engineer, quant, or in other roles requiring both technical depth and operational excellence

What Makes This Role Unique:

This is not a typical research engineering role. The work is highly operational—you'll be deeply involved in keeping our production models training smoothly, which means being responsive to incidents, flexible about priorities, and comfortable with uncertainty. During launches, the team often works extended hours and may need to respond to issues on evenings and weekends.

However, this operational intensity comes with extraordinary learning opportunities. You'll gain hands-on experience with some of the largest, most sophisticated training runs in the industry. You'll work alongside world-class researchers and engineers, and the institutional knowledge you build will compound in ways that can't be easily transferred. For people who thrive on this type of work, it's uniquely rewarding.

We're building a close-knit team of people who genuinely care about doing excellent work together. If you're someone who wants to be part of training the models that will define the future of AI—and you're excited about the full reality of what that entails—we'd love to hear from you.

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.

We encourage you to apply even if you do not believe you meet every single qualification.

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Anthropic's mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

About the Role:

Anthropic's ML Performance and Scaling team trains our production pretrained models, work that directly shapes the company's future and our mission to build safe, beneficial AI systems. As a Research Engineer on this team, you'll ensure our frontier models train reliably, efficiently, and at scale. This is demanding, high-impact work that requires both deep technical expertise and a genuine passion for the craft of large-scale ML systems.

This role lives at the boundary between research and engineering. You'll work across our entire production training stack: performance optimization, hardware debugging, experimental design, and launch coordination. During launches, the team works in tight lockstep, responding to production issues that can't wait for tomorrow.

Responsibilities:

• Own critical aspects of our production pretraining pipeline, including model operations, performance optimization, observability, and reliability
• Debug and resolve complex issues across the full stack—from hardware errors and networking to training dynamics and evaluation infrastructure
• Design and run experiments to improve training efficiency, reduce step time, increase uptime, and enhance model performance
• Respond to on-call incidents during model launches, diagnosing problems quickly and coordinating solutions across teams
• Build and maintain production logging, monitoring dashboards, and evaluation infrastructure
• Add new capabilities to the training codebase, such as long context support or novel architectures
• Collaborate closely with teammates across SF and London, as well as with Tokens, Architectures, and Systems teams
• Contribute to the team's institutional knowledge by documenting systems, debugging approaches, and lessons learned

You May Be a Good Fit If You:

• Have hands-on experience training large language models, or deep expertise with JAX, TPU, PyTorch, or large-scale distributed systems
• Genuinely enjoy both research and engineering work—you'd describe your ideal split as roughly 50/50 rather than heavily weighted toward one or the other
• Are excited about being on-call for production systems, working long days during launches, and solving hard problems under pressure
• Thrive when working on whatever is most impactful, even if that changes day-to-day based on what the production model needs
• Excel at debugging complex, ambiguous problems across multiple layers of the stack
• Communicate clearly and collaborate effectively, especially when coordinating across time zones or during high-stress incidents
• Are passionate about the work itself and want to refine your craft as a research engineer
• Care about the societal impacts of AI and responsible scaling

Strong Candidates May Also Have:

• Previous experience training LLM’s or working extensively with JAX/TPU, PyTorch, or other ML frameworks at scale
• Contributed to open-source LLM frameworks (e.g., open\_lm, llm-foundry, mesh-transformer-jax)
• Published research on model training, scaling laws, or ML systems
• Experience with production ML systems, observability tools, or evaluation infrastructure
• Background as a systems engineer, quant, or in other roles requiring both technical depth and operational excellence

What Makes This Role Unique:

This is not a typical research engineering role. The work is highly operational—you'll be deeply involved in keeping our production models training smoothly, which means being responsive to incidents, flexible about priorities, and comfortable with uncertainty. During launches, the team often works extended hours and may need to respond to issues on evenings and weekends.

However, this operational intensity comes with extraordinary learning opportunities. You'll gain hands-on experience with some of the largest, most sophisticated training runs in the industry. You'll work alongside world-class researchers and engineers, and the institutional knowledge you build will compound in ways that can't be easily transferred. For people who thrive on this type of work, it's uniquely rewarding.

We're building a close-knit team of people who genuinely care about doing excellent work together. If you're someone who wants to be part of training the models that will define the future of AI—and you're excited about the full reality of what that entails—we'd love to hear from you.

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.

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New York City; San Francisco

Employment Type

Full time

Location Type

Hybrid

Department

Safety Systems

Compensation

• $380K – $445K • 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.

• 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.

More details about our benefits are available to candidates during the hiring process.

This role is at-will and OpenAI reserves the right to modify base pay and other compensation components at any time based on individual performance, team or company results, or market conditions.

About the Team

Safety Systems sits at the forefront of OpenAI’s mission to build and deploy safe AGI, ensuring our most capable models can be released responsibly and for the benefit of society. Within Safety Systems, we are building a misalignment research team to focus on the most pressing problems for the future of AGI. Our mandate is to identify, quantify, and understand future AGI misalignment risks far in advance of when they can pose harm.

The work of this research taskforce spans four pillars:

1. Worst‑Case Demonstrations – Craft compelling, reality‑anchored demos that reveal how AI systems can go wrong. We focus especially on high importance cases where misaligned AGI could pursue goals at odds with human well being.

1. Adversarial & Frontier Safety Evaluations – Transform those demos into rigorous, repeatable evaluations that measure dangerous capabilities and residual risks. Topics of interest include deceptive behavior, scheming, reward hacking, deception in reasoning, and power-seeking, along with other related areas.

1. System‑Level Stress Testing – Build automated infrastructure to probe entire product stacks, assessing end‑to‑end robustness under extreme conditions. We treat misalignment as an evolving adversary, escalating tests until we find breaking points even as systems continue to improve.

1. Alignment Stress‑Testing Research – Investigate why mitigations break, publishing insights that shape strategy and next‑generation safeguards. We collaborate with other labs when useful and actively share misalignment findings to accelerate collective progress.

About the Role

We are seeking a Senior Researcher who is passionate about red‑teaming and AI safety. In this role you will design and execute cutting‑edge attacks, build adversarial evaluations, and advance our understanding of how safety measures can fail—and how to fix them. Your insights will directly influence OpenAI’s product launches and long‑term safety roadmap.

In this role, you will

• Design and implement worst‑case demonstrations that make AGI alignment risks concrete for stakeholders, focused on high stakes use cases described above.

• Develop adversarial and system‑level evaluations grounded in those demonstrations, driving adoption across OpenAI.

• Create automated tools and infrastructure to scale automated red‑teaming and stress testing.

• Conduct research on failure modes of alignment techniques and propose improvements.

• Publish influential internal or external papers that shift safety strategy or industry practice. We aim to concretely reduce existential AI risk.

• Partner with engineering, research, policy, and legal teams to integrate findings into product safeguards and governance processes.

• Mentor engineers and researchers, fostering a culture of rigorous, impact‑oriented safety work.

You might thrive in this role if you

• Already are thinking about these problems night and day, and share our mission to build safe, universally beneficial AGI and align with the OpenAI Charter.

• Have 4+ years of experience in AI red‑teaming, security research, adversarial ML, or related safety fields.

• Possess a strong research track record—publications, open‑source projects, or high‑impact internal work—demonstrating creativity in uncovering and exploiting system weaknesses.

• Are fluent in modern ML / AI techniques and comfortable hacking on large‑scale codebases and evaluation infrastructure.

• Communicate clearly with both technical and non‑technical audiences, translating complex findings into actionable recommendations.

• Enjoy collaboration and can drive cross‑functional projects that span research, engineering, and policy.

• Hold a Ph.D., master’s degree, or equivalent experience in computer science, machine learning, security, or a related field.

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