other
Posted May 1Research, Mid-Training
at Cognition
San Francisco, United StatesOn-site
Responsibilities
- 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.
- Develop new approaches when existing methods hit ceilings; we expect both rigorous empiricism and original thinking.
Requirements
- WHO WE ARE We are an applied AI lab building end-to-end software agents.
- We're the team behind Devin, the first AI software engineer, and Windsurf, an AI-native IDE.
- These products represent our vision for AI that doesn't just assist engineers, but works alongside them as a genuine teammate.
- Our team is small and talent-dense: world-class competitive programmers, former founders, and researchers from the frontier of AI, including Scale AI, Palantir, Cursor, Google DeepMind, and others.
- 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,
- A PhD is one signal among many.
- RESOURCES & ENVIRONMENT - Small, highly selective team where research and product move together; prototypes reach real deployment quickly - Compute is not a constraint: large allocations with training jobs routinely running across thousands of GPUs from day one - The environment rewards speed, autonomy, and technical depth with minimal process overhead; this is one of the most competitive and fast-moving problems in AI EQUAL OPPORTUNITY Cognition is an equal opportunity employer.