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About the Role

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 role will own the late-stage training decisions that determine what our models are fundamentally capable of, including data mix and quality uplift, annealing schedules, context length extension, capability injection across coding, math, and reasoning, and synthetic data strategies. This role involves cross-cutting work across pre-training and post-training, expecting both research and engineering.

Responsibilities

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.
Translate research insights into measurable capability gains on our agents.
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 optimize multi-stage learning rate schedules, warmup strategies, and compute allocation across training phases.
Understand how schedule choices interact with data distribution and model behavior.
Research and implement methods for extending effective context length without degrading short-context performance.
Build evals that distinguish real capability improvements from benchmark overfitting.
Close the loop between training decisions and what actually matters for Devin and our other systems in deployment.
Measure how mid-training interventions scale with compute and data.
Develop new approaches when existing methods hit ceilings.

Requirements

Deep familiarity with the LLM training pipeline end to end: pre-training data, optimization, architecture, and how mid-training and post-training interact.
Hands-on experience with continual pre-training, annealing, or late-stage data mixing for large models.
Strong intuition for data quality: what makes a dataset useful for training, how to filter and curate at scale, and how data mix choices compound across evals.
Experience developing or evaluating synthetic data pipelines for capability improvement.
Proficiency in Python and deep learning frameworks (PyTorch).
Comfortable debugging distributed training at scale.
Strong fundamentals in optimization, statistics, and ML theory.
Able to distinguish real effects from noise, instability, and overfitting.
A track record of original contributions: publications, open-source impact, or internal results that moved a capability frontier.
Comfort operating in ambiguous, fast-moving environments where the problem definition is as important as the solution.
Demonstrated capability is valued over credentials; a PhD is one signal among many.

Skills

Python
PyTorch
LLM training pipeline
Continual pre-training
Annealing
Late-stage data mixing
Data quality assessment
Synthetic data pipelines
Distributed training
Optimization
Statistics
ML theory
Context length extension

Location

Remote

Work Type

Full-time

Experience Level

Mid-level
Senior

Education Level

PhD (preferred, but not required)

About the Company

An applied AI lab building end-to-end software agents.
Makers of Devin, the first AI software engineer.
Team includes world-class competitive programmers, former founders, and leaders from AI companies.
Focuses on solving major world problems and building AI that reasons on real-world tasks.
Small, highly selective team where research and product move together.
Prototypes reach real deployment quickly.
Compute is not a constraint with large allocations and training jobs running across thousands of GPUs.
Environment rewards speed, autonomy, and technical depth with minimal process overhead.

Equal Opportunity

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.