Research, Post-Training

Mission

We are an applied AI lab building end-to-end software agents. Our vision is to create collaborative AI teammates that enable engineers to focus on more interesting problems and empower teams to strive for more ambitious goals.

About the role

Building Devin is just the first step—our hardest challenges still lie ahead. If you’re excited to solve some of the world’s biggest problems and build AI that can reason on real-world tasks, apply to join us.

Responsibilities

• Post-Training Recipe Development: Iterate on the full stack of datasets, training stages, and hyperparameters that determine model behavior. Measure how choices compound across evals and production performance, not just isolated benchmarks.
• Evaluation Design and Integrity: Build evals that actually capture what matters. The loop never ends: define, optimize, realize the gaps, and rebuild.
• Deep Understanding: When training produces results that don't make sense, you dig until you understand why. The goal isn't just to fix it; it's to carry that understanding forward to the next problem.
• Alignment and Agent Behavior: Apply and advance techniques like RLHF, RLAIF, and constitutional approaches to shape how agents reason, act, and collaborate with humans in long-horizon tasks.
• Scaling and Exploration: Measure how performance scales with data and compute, and develop new methodologies when existing ones hit ceilings. We expect both rigor and invention.

Requirements

• Exceptional Candidates Have Demonstrated A track record of advancing ML systems through post-training, alignment, or related methods: RLHF, RLAIF, preference modeling, reward learning, or equivalent
• Strong fundamentals in probability, statistics, and ML theory
• The ability to look at experimental data and distinguish real effects from noise and bugs
• Evidence of original contributions: publications at top venues, open-source impact, or equivalent industry results
• Experience with large-scale distributed training and the debugging that comes with it
• Systems-level thinking: not just model optimization, but understanding how training pipelines, data, and evaluation interact
• Comfort with ambiguity and fast-moving research environments where priorities shift quickly

Qualifications

• No specific qualifications listed

Skills

• Research and Engineering
• Probability, Statistics, and Machine Learning Theory
• Large-Scale Distributed Training
• Debugging
• Systems-Level Thinking
• Comfort with Ambiguity and Fast-Moving Research Environments

Benefits

• No specific benefits listed

Pay

• No specific pay range listed

Schedule

• No specific schedule listed

Equal Opportunity Employer

We are committed to providing reasonable accommodations for candidates with disabilities throughout the hiring process - please let us know if you need any.