Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.
About the role
The Knowledge Work team builds the training environments and evaluations that make Claude effective at real-world professional workflows — searching, analyzing, and creating across the tools and documents knowledge workers use every day. As that work scales, the systems behind it need to be as rigorous as the research itself.
As a Research Engineer on Knowledge, you'll design and run experiments that improve how Claude searches, retrieves, and reasons over information at scale. The work spans environment design, data curation, RL training, evaluation, and the infrastructure that supports it all. You'll move fluidly between these depending on what's blocking progress. You'll partner closely with researchers and other RL teams to ship capabilities that show up directly in Claude's behavior.
As our training and evaluations continue to scale, we see a strong synergy between the capabilities our models learn, the tools we build for them to use, and the tools we build for ourselves to understand it all. We own the science behind superhuman epistemics and we ensure the quality of the stack that drives it. We understand that real ownership and impact comes as much through hardening and iterating on environments as it does creating new ones.
Responsibilities
Design, build, and iterate on training environments and data pipelines that improve Claude's ability to reason over knowledge-intensive tasks
Run experiments end-to-end: form a hypothesis, build the infrastructure, train models, analyze results, and decide what to try next
Develop evaluations that meaningfully capture progress on search, retrieval, and reasoning quality
Identify failure modes in current model behavior and translate them into concrete training signals
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Collaborate closely with researchers across RL Data, post-training, and product teams to align on priorities and ship improvements
Contribute to shared infrastructure and tooling that compounds the team's velocity over time
Own a clean, canonical set of evaluation tools and processes for Knowledge Work capabilities, including the process used for model releases
Build and automate observability, dashboards, and operational tooling for our training environments and evaluation systems, with an emphasis on high signal-to-noise: a small set of trusted metrics and alerts rather than sprawling instrumentation
You may be a good fit if you
Are a highly experienced Python engineer who ships reliable, well-instrumented code that teammates trust in production
Experience designing, running, and analyzing ML experiments
Ability to work across the stack — from data pipelines to model training to evaluation
Have 5+ years of experience operating ML or distributed systems at scale
Comfort working with ambiguity and choosing the most impactful problem to tackle next
Clear written and verbal communication, especially when collaborating across time zones
Find genuine satisfaction and impact in making existing critical systems dependable
Preferred qualifications
Hands-on experience training, fine-tuning, or doing RL on large language models
Experience building evaluations for LLMs, particularly in open-ended or knowledge-intensive domains
Prior work in a research-heavy environment such as a frontier AI lab, quant research firm, or domain-focused AI startup
Published research on LLMs, RL, retrieval, or related areas
Experience with distributed training systems
Are comfortable being the long-term, context-rich owner of a system and its operational health
Representative projects
Building a training environment that teaches Claude to plan and execute multi-step research tasks against real document corpora
Designing an evaluation suite that distinguishes genuine reasoning over evidence from plausible-sounding pattern matching
Scaling long-running evals and fickle training environments that use many different tools
Curating and validating a high-quality dataset of expert research workflows for use in post-training
Diagnosing why Claude fails on a class of long-horizon retrieval tasks and proposing a training intervention, tool, or infrastructure change to fix it
The annual compensation range for this role is listed below.
For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.
Annual Salary:
$350,000—$850,000 USD
Logistics
Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience
Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience
Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position
Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.
Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.
We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.
Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.
How we're different
We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.
The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Sa
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