Research Engineer, Reward Models Platform

About Anthropic

Anthropics 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

You will deeply understand the research workflows of our Finetuning teams and automate the high-friction parts turning days of manual experimentation into hours. Youll 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. Youll 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 youll build the systems that make those workflows 10x faster. When you have bandwidth youll 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.

Were 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.

Responsibilities

• Design and build infrastructure that enables researchers to rapidly iterate on reward signals including tools for rubric development human feedback data analysis and reward robustness evaluation
• Develop systems for automated quality assessment of rewards including detection of reward hacks and other pathologies
• Create tooling that allows researchers to easily compare different reward methodologies (preference models rubrics programmatic rewards) and understand their effects
• Build pipelines and workflows that reduce toil in reward development from dataset preparation to evaluation to deployment
• Implement monitoring and observability systems to track reward signal quality and surface issues during training runs
• Collaborate with researchers to translate science requirements into platform capabilities
• Optimize existing systems for performance reliability and ease of use
• Contribute to the development of best practices and documentation for reward development workflows

You may be a good fit if you

• Have prior research experience
• Are excited to work closely with researchers and translate ambiguous requirements into well-scoped engineering projects
• Have strong Python skills
• Have experience with ML workflows and data pipelines and building related infrastructure/tooling/platforms
• Are comfortable working across the stack ranging from data pipelines to experiment tracking to user-facing tooling
• Can balance building robust maintainable systems with the need to move quickly in a research environment
• Are results-oriented with a bias towards flexibility and impact
• Pick up slack even if it goes outside your job description
• Care about the societal impacts of your work and are motivated by Anthropics mission to develop safe AI

Strong candidates may also have experience with

• Experience with ML research
• Building internal tooling and platforms for ML researchers
• Data quality assessment and pipeline optimization
• Experiment tracking evaluation frameworks or MLOps tooling
• Large-scale data processing (e.g. Spark Hive or similar)
• Kubernetes distributed systems or cloud infrastructure
• Familiarity with reinforcement learning or fine-tuning workflows

Representative projects

• Building infrastructure that allows researchers to rapidly test new rubric designs against small models before scaling up
• Developing automated systems to detect reward hacks and surface problematic behaviors during training
• Creating tooling for comparing different grading methodologies and understanding their effects on model behavior
• Building a data quality flywheel that helps researchers identify problematic transcripts and feed improvements back into the system
• Developing dashboards and monitoring systems that give researchers visibility into reward signal quality across training runs
• Streamlining dataset preparation workflows to reduce latency and operational overhead

Logistics

Education requirements: We require at least a Bachelors 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 arent 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 youre interested in this work. We think AI systems like the ones were 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 were 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. Were 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 Safety and Learning from Human Preferences.

Come work with us!

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. Guidance on Candidates AI Usage:Learn aboutour policyfor using AI in our application process