Member of Technical Staff, Research Engineering

The artificial intelligence landscape has rapidly transitioned from basic model fine-tuning to sophisticated reinforcement learning, multi-component training processes, and autonomous agentic ecosystems. For top-tier software and machine learning engineers who want to operate directly at the bleeding edge of AI research and production infrastructure, traditional roles no longer offer enough velocity or architectural freedom.

micro1—the premier AI data lab powering frontier model training and agent evaluations—is scaling its core research team. The company is actively recruiting exceptional technical talent for the Member of Technical Staff, Research Engineering position. Offering a competitive full-time salary range of $180,000 to $260,000 per year plus equity compensation and comprehensive benefits, this remote-first role places you at the intersection of fundamental reinforcement learning research and scalable systems engineering.

Whether you are an ambitious ML researcher looking to build self-contained training environments, scale episode pipelines, or architect automated evaluation systems for frontier models, this comprehensive guide covers the position's core responsibilities, technical requirements, compensation structure, and a step-by-step roadmap to successfully complete your application.

The Mission: Advancing Frontier AI Through Rigorous Research Engineering

As modern artificial intelligence models approach human-level performance across complex reasoning tasks, the bottleneck has shifted from raw parameter scale to data quality, evaluation rigor, and reinforcement learning efficiency. Generalist software engineering approaches frequently break down when attempting to construct robust training dynamics, multi-component training loops, and verifiable reward functions.

At micro1, the core mission is building the human intelligence layer and data infrastructure that empowers frontier models to reason, learn, and perform reliably. As a Member of Technical Staff in Research Engineering, you will not just maintain existing codebases; you will architect the foundational systems that drive state-of-the-art machine learning capabilities forward.

Core Responsibilities and Scope of Work:

• RL Environment Architecture: Architect and build self-contained reinforcement learning environments that accurately capture complex, real-world tasks—including custom reward functions, verifiers, and deterministic evaluation logic.
• Pipeline Scaling & Optimization: Design and scale episode pipelines and multi-component training processes (MCPs) to facilitate reproducible, high-throughput experimentation.
• Synthetic Data Generation: Build automated data generation systems that leverage synthetic data pipelines to accelerate training cycles without sacrificing dataset integrity.
• Automated Evaluation & QA: Develop and integrate AI-driven evaluation and quality assurance systems designed for automated grading, model validation, and iterative feedback loops.
• Model Fine-Tuning: Fine-tune and optimize open-source RL models using internally generated datasets and bespoke custom training strategies.
• Benchmarking Infrastructure: Establish comprehensive benchmarking frameworks to measure model capability, behavioral robustness, and data quality across diverse problem domains.

Preferred Qualifications and Background

micro1 maintains exceptionally high engineering standards for its core technical staff. Ideal candidates bring a combination of rigorous academic grounding and hands-on production experience in machine learning systems:

• Deep Expertise in Reinforcement Learning: Proven, hands-on experience in reinforcement learning, including sophisticated environment design, reward modeling, and complex training dynamics.
• Systems Scaling Track Record: A strong, demonstrable track record of building and scaling RL systems, training pipelines, and high-performance experimentation frameworks.
• Automation & Data Proficiency: Highly proficient in automation and data generation workflows, with specialized experience in synthetic data pipelines.
• ML Model Evaluation: Familiarity with automated evaluation systems, model validation frameworks, and quality assurance protocols for machine learning models.
• Fine-Tuning Competency: Practical experience fine-tuning and evaluating open-source ML models in research-driven or production environments.
• Communication & Technical Writing: Clear, concise communication skills backed by strong technical documentation and writing abilities.

Preferred Bonus Qualifications:

• Prior experience publishing benchmarks, model evaluations, or academic/industry research artifacts.
• Familiarity with established evaluation ecosystems (such as micro1 benchmarks or equivalent evaluation frameworks).
• Deep background in building scalable infrastructure specifically tailored for large-scale RL experimentation.

Comprehensive Compensation, Benefits, and Remote Culture

micro1 is committed to supporting a high-performing, remote-first workforce by offering total rewards packages designed to attract and retain elite engineering talent:

• Competitive Base Salary: Generous base compensation ranging from $180,000 to $260,000 per year, commensurate with experience and technical depth.
• Equity Compensation: All full-time employees are eligible for equity grants, allowing you to share directly in the long-term growth and success of the company.
• Performance Bonuses: Additional performance-based bonuses tied to role milestones and company objectives.
• Comprehensive Health Benefits: Up to 100% reimbursement for health insurance premiums, paid time off, and a 401(k) plan with a company match.
• Absolute Remote Flexibility: Work from anywhere within a collaborative, fast-paced, research-driven culture that prioritizes autonomy and high-impact output.

Step-by-Step Guide to Completing Your Application Successfully

Securing a core engineering position at micro1 is competitive. Follow these essential steps to ensure your application stands out during the AI recruiter screening process:

1. Highlight Your RL and ML Systems Experience: Ensure your resume explicitly details your hands-on work with reinforcement learning environments, training pipeline architecture, and model fine-tuning.
2. Showcase Technical Artifacts: Link to your GitHub repositories, open-source contributions, published research papers, or technical blog posts demonstrating your engineering depth.
3. Complete the Application Thoroughly: Provide detailed, precise answers in the application portal to showcase how your background aligns with micro1's research engineering objectives.

Take the next major step in your engineering career and help shape the foundational intelligence layer of artificial intelligence.