Member of Technical Staff (Research Engineering)
Core team
$180K - $300K/yr compensation
Required Skills
Reinforcement Learning
ML-Oriented Data Design
RL Environments
RL Workflows
About micro1
micro1 is a data engine that helps AI labs train foundational models and enterprises build AI agents. We provide frontier evaluations and reinforcement learning environments used to improve LLM capabilities, as well as contextual evaluations used to monitor and improve AI agents in enterprise settings. Our data engine includes an AI recruiter agent that sources and vets domain experts, a data platform that enables rapid production of high-quality training data, and a pipeline performance system that ensures both quality and velocity.
Our goal is to have 1 billion people doing meaningful work by contributing their expertise to the development of frontier AI models. We’ve raised $40M+ in funding, and our AI recruiter has powered more than 1 million AI-led interviews as our global network of experts expands to form the human intelligence layer for AGI.
Job Description
Job Title: Member of Technical Staff (Research Engineering)
Job Type: Full-time
Location: Remote
The Role
We are seeking a Research Engineer to operate at the frontier of Reinforcement Learning (RL), developing novel environments, training pipelines, and evaluation systems that advance the capabilities of modern AI models. This role sits at the intersection of research and production, translating experimental ideas into scalable, high-performance systems.
What You’ll Work On
- Architect self-contained RL environments that capture complex, real-world tasks, including reward functions, verifiers, and evaluation logic.
- Design and scale episode pipelines and multi-component training processes (MCPs) to support reproducible experimentation.
- Build automated data generation systems, leveraging synthetic data to accelerate training cycles without compromising quality.
- Develop and integrate AI-driven evaluation and quality assurance systems for automated grading, validation, and feedback loops.
- Fine-tune and optimize open-source RL models using internally generated datasets and custom training strategies.
- Establish benchmarking frameworks to measure model capability, robustness, and data quality across tasks.
- Contribute to the release and analysis of evaluations on internal and external benchmark platforms (e.g., micro1 benchmarks).
What We're Looking For
- Deep experience in Reinforcement Learning, including environment design and training dynamics.
- Strong track record of building and scaling RL systems, pipelines, or experimentation frameworks.
- Proficient in automation and data generation, including synthetic data pipelines.
- Familiar with automated evaluation systems, model validation, and quality assurance workflows.
- Experienced in fine-tuning and evaluating open-source ML models.
- Clear, concise communicator with strong technical writing skills.
- Comfortable operating in fast-paced, research-driven, and highly collaborative environments.
Preferred
- Experience publishing benchmarks, evaluations, or research artifacts.
- Familiarity with evaluation ecosystems (e.g., micro1 benchmarks or similar frameworks).
- Background in scalable infrastructure for large-scale RL experimentation.