Research, Mid-Training

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 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.
• Annealing and Schedule Design: 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.
• Context Length Extension: Research and implement methods for extending effective context length without degrading short-context performance. This includes positional encoding strategies, data construction, and targeted evaluation.
• Evaluation and Iteration: 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.
• Scaling and Methodology: Measure how mid-training interventions scale with compute and data. Develop new approaches when existing methods hit ceilings; we expect both rigorous empiricism and original thinking.

Exceptional Candidates Have Demonstrated

• 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