Primary record

Research Engineer, Mid-Training

Cognition Indexed employerSan Francisco
Source-hosted applyChecked 45m agoFull-Time
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Workplace

On-site

Employment

Full-Time

Published

Aug 6, 2026

Closes

No date supplied

The role

WE ARE AN APPLIED AI LAB BUILDING END-TO-END SOFTWARE AGENTS. We're the makers of Devin, the first AI software engineer. Our team is extremely talent-dense. Among our founding team, we have world-class competitive programmers, former founders, and leaders from companies at the cutting edge of AI including Scale AI, Palantir, Cursor, Waymo, Tesla, Lunchclub, Modal, Google DeepMind, and Nuro. 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. ROLE MISSION Mid-training sits at the seam between pre-training and post-training and is one of the highest-leverage points in the entire model pipeline. This is where raw base model capability is sharpened into something that can reason deeply, generalize reliably, and serve as the foundation that post-training builds on. You will own the late-stage training decisions that determine what our models are fundamentally capable of: data mix and quality uplift, annealing schedules, context length extension, capability injection across coding, math, and reasoning, and the synthetic data strategies that make all of it scale. This role does cross-cutting work across what is classically considered both pre-training and post-training. We don't distinguish between research and engineering; we expect both. WHAT YOU'LL ACCOMPLISH - Data Mix and Quality Uplift: Design and iterate on high-quality data mixtures for late-stage and annealing training runs. Develop principled methods for sourcing, filtering, and weighting data to sharpen model capabilities without degrading general performance. - Capability Injection: Drive targeted improvements in coding, mathematics, and long-horizon reasoning through curated data strategies and training interventions. Translate research insights into measurable capability gains on our agents. - Synthetic Data Research: Develop and evaluate synthetic data

Requirements

Department: Research & Development; Team: Research