Research Engineer, Reward Models Platform

Job Description

About AnthropicAnthropic’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 roleYou will deeply understand the research workflows of our Finetuning teams and automate the high-friction parts – turning days of manual experimentation into hours. You’ll build the tools and infrastructure that enable researchers across the organization to develop, evaluate, and optimize reward signals for training our models. Yourscalable platforms will make it easy to experiment with different reward methodologies, assess their robustness, and iterate rapidly on improvements to help the rest of Anthropic train our reward models. This is a role for someone who wants to stay close to the science while having outsized leverage.

You’ll partner directly with researchers on the Rewards team and across the broader Fine-Tuning organization to understand what slows them down: running human data experiments before adding to preference models, debugging reward hacks, comparing rubric methodologies across domains. Then you’ll build the systems that make those workflows 10x faster.

When you have bandwidth, you’ll contribute directly to research projects yourself. Your work will directly impact our ability to scale reward development across domains, from crafting and evaluating rubrics to understanding the effects of human feedback data to detecting and mitigating reward hacks. We’re looking for someone who combines strong engineering fundamentals with research experience – someone who can scope ambiguous problems, ship quickly, and cares as much about the science as the systems.

Note: For this role, we conduct all interviews in Python. ResponsibilitiesDesign and build infrastructure that enables researchers to rapidly iterate on reward signals, including tools for rubric development, human feedback data analysis, and reward robustness evaluationDevelop systems for automated quality assessment of rewards, including detection of reward hacks and other pathologiesCreate tooling that allows researchers to easily compare different reward methodologies (preference models, rubrics, programmatic rewards) and understand their effectsBuild pipelines and workflows that reduce toil in reward development, from dataset preparation to evaluation to deploymentImplement monitoring and observability systems to track reward signal quality and surface issues during training runsCollaborate with researchers to translate science requirements into platform capabilitiesOptimize existing systems for performance, reliability, and ease of useContribute to the development of best practices and documentation for reward development workflowsYou may be a good fit if youHave prior research experience Are excited to work closely with researchers and translate ambiguous requirements into well-scoped engineering projectsHave strong Python skillsHave experience with ML workflows and data pipelines, and building related infrastructure/tooling/platformsAre comfortable working across the stack, ranging from data pipelines to experiment tracking to user-facing toolingCan balance building robust, maintainable systems with the need to move quickly in a research environmentAre results-oriented, with a bias towards flexibility and impactPick up slack, even if it goes outside your job descriptionCare about the societal impacts of your work and are motivated by Anthropic’s mission to develop safe AIStrong candidates may also have experience withExperience with ML researchBuilding internal tooling and platforms for ML researchersData quality assessment and pipeline optimizationExperiment tracking, evaluation frameworks, or MLOps toolingLarge-scale data processing (e. g.

, Spark, Hive, or similar)Kubernetes, distributed systems, or cloud infrastructureFamiliarity with reinforcement learning or fine-tuning workflows Representative projectsBuilding infrastructure that allows researchers to rapidly test new rubric designs against small models before scaling upDeveloping automated systems to detect reward hacks and surface problematic behaviors during trainingCreating tooling for comparing different grading methodologies and understanding their effects on model behaviorBuilding a data quality flywheel that helps researchers identify problematic transcripts and feed improvements back into the systemDeveloping dashboards and monitoring systems that give researchers visibility into reward signal quality across training runsStreamlining dataset preparation workflows to reduce latency and operational overheadThe expected base compensation for this position is below. Our total compensation package for full-time employees includes equity, benefits, and may include incentive compensation.

Annual Salary:$315,000 – $340,000 USDLogisticsEducation 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. 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. How we’re differentWe believe that the highest-impact AI research will be big science.

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