Research Engineer, Reinforcement Learning
Listing checked September 15, 2026 · pay as published by Micro1
Overview
micro1 is hiring a Research Engineer to work at the frontier of Reinforcement Learning. You will build novel RL environments, training pipelines, and evaluation systems that push the limits of modern AI models. This role blends research and production, turning experimental ideas into scalable, high-performance systems. The position is full-time and remote, with a national pay range of $180,000 to $260,000 USD.
What You'll Do7
- 1Architect self-contained RL environments that mirror complex, real-world tasks, complete with reward functions, verifiers, and evaluation logic.
- 2Design and scale episode pipelines and multi-component training processes (MCPs) to enable reproducible experimentation.
- 3Build automated data generation systems that use synthetic data to speed up training cycles while maintaining quality.
- 4Develop and integrate AI-driven evaluation and quality assurance systems for automated grading, validation, and feedback loops.
- 5Fine-tune and optimize open-source RL models using internally generated datasets and custom training strategies.
- 6Establish benchmarking frameworks to measure model capability, robustness, and data quality across tasks.
- 7Contribute to the release and analysis of evaluations on internal and external benchmark platforms, such as micro1 benchmarks.
Requirements7
- 1Deep experience in Reinforcement Learning, including environment design and training dynamics.
- 2Strong track record of building and scaling RL systems, pipelines, or experimentation frameworks.
- 3Proficient in automation and data generation, including synthetic data pipelines.
- 4Familiar with automated evaluation systems, model validation, and quality assurance workflows.
- 5Experienced in fine-tuning and evaluating open-source ML models.
- 6Clear, concise communicator with strong technical writing skills.
- 7Comfortable operating in fast-paced, research-driven, and highly collaborative environments.
Who Should Apply
This role suits engineers with a deep background in Reinforcement Learning, especially those who have designed RL environments and scaled training pipelines. Candidates who have fine-tuned open-source models and built automated evaluation systems will find a strong fit. The position is less suitable for those without hands-on RL experience or who prefer purely theoretical work. A common reason candidates get rejected is failing to show concrete examples of scaling RL systems or synthetic data pipelines. Another is weak technical writing, since clear documentation is essential for this collaborative, research-driven role.
Salary Insight
The national pay range for this full-time position is a base salary of $180,000 to $260,000 USD. Employees are eligible for equity compensation and may receive performance-based bonuses. Benefits include up to 100% reimbursement for health-insurance premiums, paid time off, a 401(K) plan with a company match, and additional support for a remote-first workforce.
Pay and demand for Machine Learning & AI roles
AggregatedTypical pay
$75/hour
This role
$7–$8/hr
Most Machine Learning & AI roles pay $55–$105 per hour. This role's pay sits below that range.
Based on 444 similar roles that publish pay · 91 publish only a top rate; those count at the rate they gave
Rates shown per hour. Yearly and monthly pay converted; one-time fees and non-USD pay are not included.
- Live similar roles
- 513
- Listed in last 30 days
- 333
- Remote
- 97%
Hiring most right now: micro1 (208) · Mercor (71) · SME Careers (62)
Most requested skills · share of roles
- python20%
- technical writing10%
- ai training8%
- ai evaluation8%
Figures from Machine Learning & AI roles live on NearSkill when this page loaded. A role can close before you apply, so check the listing itself.
Compare your resume against these rolesLocation
Compensation
$7–8/hr
Required Skills
Application Tip
Highlight specific projects where you designed RL environments or scaled training pipelines. Quantify results, such as the number of episodes generated or improvements in model performance, and mention any experience with synthetic data or benchmark publishing.
See NearSkill jobs more often in your search
Application & verification flow
1Instant rubric match
Your resume is scanned against this role’s requirements to check qualification fit.
2Screened before the employer sees it
Only profiles that clear screening are passed on.
3Outcome by email
We notify you at the address on your resume once the screening is reviewed.
Similar open positions
Explore active roles that match your skills and interests.
Micro1
VerifiedMember of Technical Staff, Research Engineering
This role places you at the cutting edge of Reinforcement Learning, where you'll build novel training environments, scalable pipelines, and robust evaluation frameworks that push the boundaries of AI. You'll bridge the gap between research and production, turning experimental concepts into high-performance systems that power real-world applications. As a key member of the research engineering team, you'll own the full lifecycle of RL system development, from environment design to model fine-tuning.
Micro1
VerifiedSenior Backend Engineer for RL Environments
Senior Backend Engineer, contracting 20 hours per week, remote work for micro1. You’ll join a high‑level project to build and test Reinforcement Learning Environments used for AI model training and evaluation. Your backend and DevOps expertise will shape how models learn, reason, and perform under realistic cloud conditions. Prior AI experience isn’t required; deep domain knowledge in distributed systems matters most. The work centers on creating scalable, reproducible environments with deterministic tests and clear documentation, covering production‑grade cloud infrastructure, security, and recoverability. In short, you’ll craft complex testbeds that push AI systems to their limits using real‑world infrastructure patterns, with an emphasis on reproducibility and reliability.
Micro1
VerifiedSenior Software Engineer
micro1 is hiring Senior Software Engineers to build Reinforcement Learning Environments that test how well AI models handle real coding problems. You will design tasks around bug fixing, feature implementation, refactoring, and performance tuning, then create a reproducible setup and a golden reference solution for each. This is a remote contractor role at roughly 15 hours per week, and no prior AI experience is needed. Your software engineering depth is what matters here.
Micro1
VerifiedMember of Technical Staff, Frontier AI
micro1 seeks a Member of Technical Staff to own research and evaluation projects that sharpen model and system performance. You will design ML-oriented data systems, run failure analysis, and build evaluation frameworks that turn messy real-world behavior into structured signal. Working alongside researchers, domain experts, and operators, you will act as a quality gate, deciding when findings are solid enough to share. This full-time remote role pays $100 to $130 per hour.
Micro1
VerifiedGitHub Contributor for AI Training (Remote, Contract)
micro1 is hiring expert Senior Software Engineers to help train next-generation AI systems by building Reinforcement Learning Environments. You will create reproducible coding challenges and golden reference solutions that test an AI model's ability to fix bugs, add features, refactor code, and improve performance. This is a remote contractor role, roughly 15 hours per week, with compensation of $50 to $100 per hour based on output. No prior AI experience is needed. Your deep software engineering skills in languages like Python, Java, Rust, Go, C++, or TypeScript are what matter.
Micro1
VerifiedMember of Technical Staff, Robotics Research
Join micro1 as a remote full-time Robotics Researcher and help shape the future of robotics data. You’ll explore how datasets, collection methods, and evaluation frameworks support next‑generation robot learning and perception. You’ll work with top AI and robotics teams to turn research challenges into scalable data solutions for real‑world physical AI. Expect to contribute to world models, egocentric video, robot trajectories, sensors, and simulation data strategies. Bold Robotics Data, Embodied AI, and data pipelines as part of your daily focus.