Staff+ Software Engineer, RL Data Platform
Anthropic
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Benefits offered by this job
Job summary
Anthropic is seeking an experienced engineer to join the RL Data Platform team in a hybrid San Francisco setting. You will own full-stack data interfaces, back-end services, and data pipelines used by RL researchers to collect and feed high-quality human data into training.
You’ll design, ship, and operate systems that support large-scale annotations and model evaluation. You will work with researchers to understand data needs, ship reliable data tooling, and ensure data quality and
Qualifications
- Strong full-stack skills with production experience in TS/React frontend and Python backend.
- Experience designing and operating backend services and data pipelines.
- A track record of owning projects end-to-end from brief to production.
- Comfort collaborating with technical stakeholders and researchers.
- Experience using AI tools in daily work.
- Interest in the societal impacts of AI.
Responsibilities
- Design, build, and operate the feedback and data collection interfaces for annotators and researchers.
- Develop backend services, APIs, and pipelines that route model samples and collect data.
- Maintain reliability, latency, and usability of live systems.
- Translate research data needs into well-scoped data collection campaigns.
- Create dashboards and inspection tools to monitor data quality and throughput.
- Identify bottlenecks between data requests and training integration.
Skills
Education
Job description
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.
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.
Anthropic’s RL Data Platform team builds the systems that produce, move, and serve the human data Claude learns from: the interfaces humans use to give feedback, the pipelines that turn raw feedback into training signal, and the tooling researchers use to launch, monitor, and inspect data collection. Every RL run depends on a steady supply of high‑quality data - human feedback, expert demonstrations, graded transcripts - and when a researcher has an idea for new data on Monday, our job is to make it collectable by Wednesday and in the training mix by Friday. This is a full-stack, ownership‑heavy role on a small, senior team. You’ll design and ship web interfaces used by thousands of expert annotators, build the backend services and data pipelines behind them, and work directly with RL researchers to understand what data they need and why. You’ll scope your own projects, make architectural calls, and see them through to production. We’re looking for engineers who treat researchers as their users, build for reliability first, and care as much about the shape of the data leaving the system as the UI going into it.
- Design, build, and operate the feedback and data collection interfaces used by human annotators, domain experts, and internal researchers.
- Build and maintain the backend services, APIs, and pipelines that route model samples to humans and return structured feedback to training.
- Own the reliability, latency, and usability of systems that run continuously against live model endpoints.
- Partner with RL researchers to translate loosely specified data needs into well‑scoped collection campaigns and the tooling to run them.
- Build dashboards, monitoring, and inspection tools so researchers can see data quality and throughput without asking an engineer.
- Identify and remove the bottlenecks between "we want this data" and "it's in the training mix".
- Strong full-stack engineering skills, with production experience in TypeScript/React on the frontend and Python on the backend.
- Experience designing and operating backend services and data pipelines that other teams depend on.
- A track record of owning projects end-to-end, from an ambiguous brief to something in production that people use.
- Comfort working directly with technical stakeholders whose needs change week to week, and the judgment to push back when something isn’t worth building.
- Effective use of AI tools in your own day-to-day work.
- Care about the societal impacts of your work.
- Experience building annotation, labelling, evaluation, or other human-in-the-loop data tooling.
- Experience with RLHF, preference data, or other human-feedback pipelines for ML systems.
- Experience shipping researcher-facing or other expert-facing internal tools people love: interviewing users, hunting down friction, measurably improving the experience.
- Experience running experiments on data collection interfaces and using the results to improve data quality.
- Experience working with crowdworker or expert vendor platforms at scale.
- Familiarity with how LLMs are trained and evaluated.
- Build an interface that lets a domain expert review a long agentic transcript, flag the step where things went wrong, and write a corrected continuation - with the result landing in a training-ready format.
- Rework the sampling path between our feedback interfaces and model endpoints to cut time-to-first-sample for annotators.
- Build a campaign launcher that lets a researcher stand up a new data collection effort (task, rubric, population, quality checks) without writing code.
- Instrument annotator behaviour to detect low-effort or adversarial work and surface it to the quality team automatically.
- Design the data model for a kind of feedback we haven’t collected before, and ship the pipeline that gets it into the training mix.
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.
$320,000—$405,000 USD
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 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 Safety, and Learning from Human Preferences.
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.
Learn about our policy for using AI in our application process.
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