Research Engineer, Model Evaluations

div class= content-intro h2 strong About Anthropic /strong /h2

p 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. /p /div h2 class= text-text-100 mt-3 -mb-1 text-[1.125rem] font-bold data-sourcepos= 3:1-3:18;40-57 About the role /h2

p class= font-claude-response-body break-words whitespace-normal leading-[1.7] data-sourcepos= 5:1-5:267;59-325 We're looking for Research Engineers to build the evaluations that tell us — and the world — what Claude can actually do. Your work will turn ambiguous notions of intelligence into clear, defensible metrics that researchers, leadership, and the public can rely on. /p

p class= font-claude-response-body break-words whitespace-normal leading-[1.7] data-sourcepos= 7:1-7:548;327-874 You'll design and implement evaluations across the full spectrum of Claude's capabilities and personality, and build the infrastructure that runs them reliably at scale. You'll partner closely with researchers throughout the lifecycle of a new capability — from defining what to measure, to running the eval against live training checkpoints, to interpreting the results. The goal is to make Anthropic the leader in extremely well-characterized AI systems, with performance that is exhaustively measured and validated across the tasks that matter. /p

h2 class= text-text-100 mt-3 -mb-1 text-[1.125rem] font-bold data-sourcepos= 9:1-9:24;876-899 Key responsibilities /h2

ul class= [li_ ]:mb-0 [li_ ]:mt-1 [li_ ]:gap-1 [ :not(:last-child)_ul]:pb-1 [ :not(:last-child)_ol]:pb-1 list-disc flex flex-col gap-1 pl-8 mb-3 data-sourcepos= 11:1-18:112;901-2127

li class= whitespace-normal break-words pl-2 data-sourcepos= 11:1-11:212;901-1112 Design and run new evaluations of Claude's capabilities — reasoning, agentic behavior, knowledge, safety properties — and produce visualizations that make the results legible to researchers and decision-makers /li

li class= whitespace-normal break-words pl-2 data-sourcepos= 12:1-12:152;1113-1264 Build and harden the distributed eval execution platform so hundreds of evals run reliably against checkpoints throughout production RL training runs /li

li class= whitespace-normal break-words pl-2 data-sourcepos= 13:1-13:180;1265-1444 Own the dashboards researchers and leadership use to monitor model health during training, improving signal-to-noise, reducing latency, and making regressions impossible to miss /li

li class= whitespace-normal break-words pl-2 data-sourcepos= 14:1-14:178;1445-1622 Debug anomalous eval results mid-training-run, determine whether the cause is a model change or an infrastructure issue, and communicate the answer clearly under time pressure /li

li class= whitespace-normal break-words pl-2 data-sourcepos= 15:1-15:104;1623-1726 Improve the tooling, libraries, and workflows researchers use to implement and iterate on evaluations /li

li class= whitespace-normal break-words pl-2 data-sourcepos= 16:1-16:155;1727-1881 Partner with research teams across the full lifecycle of a new capability — from defining what to measure to interpreting results as training progresses /li

li class= whitespace-normal break-words pl-2 data-sourcepos= 17:1-17:134;1882-2015 Run experiments to characterize how prompting, sampling, and scaffolding choices affect results on internal and industry benchmarks /li

li class= whitespace-normal break-words pl-2 data-sourcepos= 18:1-18:112;2016-2127 Communicate evaluations and their results to internal stakeholders and, where appropriate, external audiences /li

/ul

h2 class= text-text-100 mt-3 -mb-1 text-[1.125rem] font-bold data-sourcepos= 20:1-20:26;2129-2154 Minimum qualifications /h2

ul class= [li_ ]:mb-0 [li_ ]:mt-1 [li_ ]:gap-1 [ :not(:last-child)_ul]:pb-1 [ :not(:last-child)_ol]:pb-1 list-disc flex flex-col gap-1 pl-8 mb-3 data-sourcepos= 22:1-26:113;2156-2682

li class= whitespace-normal break-words pl-2 data-sourcepos= 22:1-22:84;2156-2239 Strong Python programming skills, including production or research infrastructure /li

li class= whitespace-normal break-words pl-2 data-sourcepos= 23:1-23:131;2240-2370 Experience building or operating distributed systems, data pipelines, or other infrastructure that needs to be reliable at scale /li

li class= whitespace-normal break-words pl-2 data-sourcepos= 24:1-24:106;2371-2476 Clear written and verbal communication, especially when explaining technical results to non-specialists /li

li class= whitespace-normal break-words pl-2 data-sourcepos= 25:1-25:93;2477-2569 Comfort operating in an on-call or production-support capacity when training runs are live /li

li class= whitespace-normal break-words pl-2 data-sourcepos= 26:1-26:113;2570-2682 Care about the societal impacts of your work and an interest in steering powerful AI to be safe and beneficial /li

/ul

h2 class= text-text-100 mt-3 -mb-1 text-[1.125rem] font-bold data-sourcepos= 28:1-28:28;2684-2711 Preferred qualifications /h2

ul class= [li_ ]:mb-0 [li_ ]:mt-1 [li_ ]:gap-1 [ :not(:last-child)_ul]:pb-1 [ :not(:last-child)_ol]:pb-1 list-disc flex flex-col gap-1 pl-8 mb-3 data-sourcepos= 30:1-38:43;2713-3386

li class= whitespace-normal break-words pl-2 data-sourcepos= 30:1-30:113;2713-2825 Hands-on experience using large language models such as Claude, including prompting, sampling, and scaffolding /li

li class= whitespace-normal break-words pl-2 data-sourcepos= 31:1-31:107;2826-2932 Background in data visualization and a track record of building dashboards people actually trust and use /li

li class= whitespace-normal break-words pl-2 data-sourcepos= 32:1-32:70;2933-3002 Experience developing robust evaluation metrics for language models /li

li class= whitespace-normal break-words pl-2 data-sourcepos= 33:1-33:76;3003-3078 Experience with observability, monitoring, or experiment-tracking systems /li

li class= whitespace-normal break-words pl-2 data-sourcepos= 34:1-34:51;3079-3129 Background in statistics and experimental design /li

li class= whitespace-normal break-words pl-2 data-sourcepos= 35:1-35:73;3130-3202 Experience with large-scale dataset sourcing, curation, and processing /li

li class= whitespace-normal break-words pl-2 data-sourcepos= 36:1-36:62;3203-3264 Experience running or supporting ML training infrastructure /li

li class= whitespace-normal break-words pl-2 data-sourcepos= 37:1-37:79;3265-3343 A bias toward picking up slack and operating flexibly across team boundaries /li

li class= whitespace-normal break-words pl-2 data-sourcepos= 38:1-38:43;3344-3386 Enjoy pair programming — we love to pair /li

/ul

h2 class= text-text-100 mt-3 -mb-1 text-[1.125rem] font-bold data-sourcepos= 40:1-40:27;3388-3414 Representative projects /h2

ul class= [li_ ]:mb-0 [li_ ]:mt-1 [li_ ]:gap-1 [ :not(:last-child)_ul]:pb-1 [ :not(:last-child)_ol]:pb-1 list-disc flex flex-col gap-1 pl-8 mb-3 data-sourcepos= 42:1-45:151;3416-4106

li class= whitespace-normal break-words pl-2 data-sourcepos= 42:1-42:222;3416-3637 Stand up a new eval that tests a specific reasoning capability from scratch — define the task, build the dataset, implement the scoring, validate against known signals, and ship a dashboard that makes the result legible /li

li class= whitespace-normal break-words pl-2 data-sourcepos= 43:1-43:187;3638-3824 Diagnose a mid-training regression: an eval suite returns anomalous numbers, and you need to determine within hours whether it's the model, the harness, the data, or the infrastructure /li

li class= whitespace-normal break-words pl-2 data-sourcepos= 44:1-44:131;3825-3955 Take a flaky distributed eval pipeline and make it boring — better retries, better observability, faster feedback to researchers /li

li class= whitespace-normal break-words pl-2 data-sourcepos= 45:1-45:151;3956-4106 Partner with a research team on a new capability area, helping them articulate what good looks like and translating that into measurable artifacts /li

/ul div class= content-pay-transparency div class= pay-input div class= description p The annual compensation range for this role is listed below. /p

p 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. /p /div div class= title Annual Salary: /div div class= pay-range span $500,000 /span span class= divider /span span $850,000 USD /span /div /div /div div class= content-conclusion h2 strong Logistics /strong /h2

p strong Minimum education: /strong Bachelor’s degree or an equivalent combination of education, training, and/or experience /p

p strong Required field of study: /strong A field relevant to the role as demonstrated through coursework, training, or professional experience /p

p strong Minimum years of experience: /strong Years of experience required will correlate with the internal job level requirements for the position /p

p strong Location-based hybrid policy: /strong 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. /p

p strong data-stringify-type= bold Visa sponsorship: /strong 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. /p

p strong We encourage you to apply even if you do not believe you meet every single qualification. /strong 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. br br strong data-stringify-type= bold Your safety matters to us. /strong 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 u data-stringify-type= underline a class= c-link c-link--underline href= http://anthropic.com/careers target= _blank data-stringify-link= http://anthropic.com/careers data-sk= tooltip_parent data-remove-tab-index= true anthropic.com/careers /a /u directly for confirmed position openings. /p

h2 strong How we're different /strong /h2

p 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. /p

p The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. /p

h2 strong Come work with us! /strong /h2

p Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. strong data-stringify-type= bold Guidance on Candidates' AI Usage: /strong Learn about a class= c-link href= https://www.anthropic.com/candidate-ai-guidance target= _blank data-stringify-link= https://www.anthropic.com/candidate-ai-guidance data-sk= tooltip_parent our policy /a for using AI in our application process. /p /div