# Staff+ Research Engineer, RL Data Platform

**Company:** [Anthropic](https://hotfix.jobs/companies/anthropic)
**Location:** San Francisco, CA, New York, NY
**Role:** Fullstack Engineering
**Salary:** $500k – $850k/yr
**Experience:** 7+ years
**Skills:** TypeScript, React, Python, Backend Services, APIs, Data Pipelines, Machine Learning, RLHF, LLMs, Data Collection, Human-In-The-Loop Systems, Monitoring
**Posted:** 2026-08-27

> Staff-level full-stack engineer building human-feedback interfaces, backend services, and data pipelines that supply reinforcement-learning training data. The role partners closely with RL researchers and requires production TypeScript/React and Python experience.

## Job Description

## Responsibilities
- Design, build, and operate feedback and data-collection interfaces for human annotators, domain experts, and internal researchers.
- Build and maintain backend services, APIs, and data pipelines that route model samples to humans and return structured feedback for training.
- Own the reliability, latency, and usability of systems operating continuously against live model endpoints.
- Partner with RL researchers to translate data needs into collection campaigns and supporting tooling.
- Build dashboards, monitoring, and inspection tools for data quality and throughput.
- Identify and remove bottlenecks between data requests and inclusion in the training mix.

## Requirements
- Strong full-stack engineering skills with production experience in TypeScript, React, and Python.
- Experience designing and operating backend services and data pipelines used by other teams.
- Experience owning projects end-to-end, from ambiguous requirements through production.
- Ability to work directly with technical stakeholders and exercise product and architectural judgment.
- Effective use of AI tools in day-to-day work.
- Care for the societal impacts of the work.
- Bachelor's degree or equivalent combination of education, training, and experience in a relevant field.

## Nice-to-haves
- Experience building annotation, labeling, evaluation, or other human-in-the-loop data tooling.
- Experience with RLHF, preference data, or human-feedback pipelines for ML systems.
- Experience shipping researcher-facing or expert-facing internal tools.
- Experience experimenting with data-collection interfaces to improve data quality.
- Experience working with crowdworker or expert vendor platforms at scale.
- Familiarity with LLM training and evaluation.

## Compensation
- Annual salary: $500,000–$850,000 USD.

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