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Member of Technical Staff, Research Engineering
micro1 is hiring a Member of Technical Staff, Research Engineering to work at the frontier of Reinforcement Learning, developing novel RL environments, training pipelines, data generation systems, and evaluation frameworks that advance modern AI models. This full-time role bridges research and production by translating experimental ideas into scalable, high-performance systems. The work includes designing self-contained RL environments, scaling experimentation pipelines, generating synthetic training data, building automated evaluation systems, fine-tuning open-source models, and establishing rigorous benchmarking frameworks.
Salary
USD180,000 - USD260,000 / year
Department
Research Engineering & Reinforcement Learning
Experience
Deep experience in reinforcement learning with a strong track record of designing RL environments and building or scaling training systems, experimentation pipelines, automated evaluation workflows, and ML-oriented data generation systems
Curated by Farooq77 Jobs
Role fit snapshot
- Engagement
- Full-time remote | micro1 application platform
- Experience
- Recorded experience: Deep experience in reinforcement learning with a strong track record of designing RL environments and building or scaling training systems, experimentation pipelines, automated evaluation workflows, and ML-oriented data generation systems
- Core expertise
- Research Engineering & Reinforcement Learning | Reinforcement Learning | RL Environment Design | RL Training
- Geographic eligibility
- Not specified beyond Remote
- Listing dates
- Posted 2026-09-16 | Valid through 2026-10-16
- Compensation
- Recorded compensation: $180,000-$260,000/year
Responsibilities
- Architect self-contained reinforcement learning environments representing complex real-world tasks.
- Design reward functions, verifiers, and evaluation logic for RL environments.
- Design and scale episode pipelines and multi-component training processes to support reproducible experimentation.
- Build automated data generation systems using synthetic data to accelerate model training while maintaining data quality.
- Develop and integrate AI-driven evaluation and quality assurance systems for automated grading, validation, and feedback loops.
- Fine-tune and optimize open-source reinforcement learning models using internally generated datasets and custom training strategies.
- Establish benchmarking frameworks for measuring model capability, robustness, and data quality across different tasks.
- Build and improve scalable RL systems, training pipelines, and experimentation frameworks.
- Contribute to the release and analysis of evaluations on internal and external benchmark platforms.
- Translate experimental research ideas into scalable, high-performance production systems.
Skills
Reinforcement LearningRL Environment DesignRL TrainingRL WorkflowsRL Training DynamicsML-Oriented Data DesignSynthetic Data GenerationAutomated Data GenerationEpisode PipelinesMulti-Component Training ProcessesReward FunctionsVerifiersEvaluation LogicAI EvaluationAutomated EvaluationModel ValidationQuality AssuranceModel Fine-TuningOpen-Source ML ModelsBenchmarkingEvaluation FrameworksTraining PipelinesExperimentation FrameworksModel Robustness EvaluationData Quality EvaluationScalable ML InfrastructureTechnical WritingResearch Engineering
Requirements
- Deep experience in reinforcement learning, including RL environment design and training dynamics.
- Strong track record of building and scaling reinforcement learning systems, pipelines, or experimentation frameworks.
- Proficiency in automation and data generation, including synthetic data pipelines.
- Familiarity with automated evaluation systems, model validation, and quality assurance workflows.
- Experience fine-tuning and evaluating open-source machine learning models.
- Ability to design reproducible experimentation and training workflows.
- Strong understanding of benchmarking, model capability evaluation, robustness assessment, and data quality.
- Clear and concise communication skills with strong technical writing ability.
- Ability to operate effectively in fast-paced, research-driven, and highly collaborative environments.
- Experience publishing benchmarks, evaluations, or research artifacts is preferred.
- Familiarity with evaluation ecosystems such as micro1 benchmarks or comparable frameworks is preferred.
- Experience with scalable infrastructure for large-scale reinforcement learning experimentation is preferred.
Benefits
- Full-time remote position.
- Base salary of $180,000–$260,000 per year.
- Eligibility for equity compensation.
- Potential performance-based bonuses subject to role and company policies.
- Up to 100% reimbursement for health insurance premiums.
- Paid time off.
- 401(k) plan with company match.
- Additional benefits supporting a high-performing remote-first workforce.
- Opportunity to work at the frontier of reinforcement learning research and engineering.
- Opportunity to develop RL environments, training pipelines, synthetic data systems, and evaluation frameworks for advanced AI models.
Before you apply
- Confirm that your location is eligible for the role.
- Confirm the employment or contract type.
- Verify the current compensation at the official source.
- Review the required skills and experience.
- Check that the listing is still open before applying.
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