Cognition

Research, Mid-Training

San Francisco · Mid

Sponsorship not specifiedDetected 82 days ago

PythonMachine LearningDeep LearningPyTorchData EngineeringLLMsStatisticsResearch

About the role

We're the makers of Devin, the first AI software engineer.
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.
This role does cross-cutting work across what is classically considered both pre-training and post-training.

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.
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.
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.
Drive targeted improvements in coding, mathematics, and long-horizon reasoning through curated data strategies and training interventions.
Develop and evaluate synthetic data pipelines that generate training signal at scale.
Understand the limits and failure modes of synthetic approaches and build methods that hold up in production training runs.
Research and implement methods for extending effective context length without degrading short-context performance.

Hands-on experience with continual pre-training, annealing, or late-stage data mixing for large models

Research and optimize multi-stage learning rate schedules, warmup strategies, and compute allocation across training phases.

Cognition is an equal opportunity employer.
We do not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other protected characteristic under applicable law.
We are committed to providing reasonable accommodations for candidates with disabilities throughout the hiring process - please let us know if you need any.

This listing is sourced directly from Cognition's careers page and normalized into a canonical job model.