Research Engineer, Mid-Training

Highlights

• Own mid-training decisions that sharpen base models into deep reasoning agents.
• Drive coding, math and long-horizon reasoning gains powering Devin in deployment.
• Research annealing schedules and synthetic data that scale across thousands of GPUs.

Ideal candidate

Empirically-driven researcher-engineer who thrives in ambiguity and cares about turning training insights into real reasoning gains for deployed agents.

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