Job Summary:
Cognition is an applied AI lab focused on building end-to-end software agents. The role involves owning late-stage training decisions for AI models, emphasizing data quality, capability injection, and synthetic data research to enhance model performance.
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.
• This includes positional encoding strategies, data construction, and targeted evaluation.
• 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; we expect both rigorous empiricism and original thinking.
Qualifications:
Required:
• 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
• We care more about demonstrated capability than credentials. A PhD is one signal among many.
Company:
Cognition develops artificial intelligence systems for software engineering and code automation. Founded in 2023, the company is headquartered in San Francisco, USA, with a team of 51-200 employees. The company is currently Growth Stage.