Design, train, and evaluate machine learning models across a range of research and applied AI initiatives
Run rapid, iterative experiments to test hypotheses and surface insights that drive model improvements
Collaborate closely with researchers and engineers to translate cutting-edge academic advances into practical, production-ready systems
Build and maintain robust ML pipelines for data ingestion, feature engineering, model training, and evaluation
Optimize model performance through fine-tuning, hyperparameter search, and architecture experimentation
Contribute to a culture of rigorous experimentation; tracking results, documenting findings, and sharing learnings with the broader team
Stay current with the latest developments in ML and AI research, and proactively identify opportunities to apply them
This position may require stand-by, on-call, or off-hours duties during critical research or deployment milestones

Bachelor's degree in Computer Science, Mathematics, Statistics, or a related field with 5+ years of industry or research experience (Master's or PhD a plus)
Deep hands-on experience training and evaluating ML models, including language models
Strong proficiency in Python and ML frameworks such as PyTorch or TensorFlow
Familiarity with MLOps tooling and infrastructure (e.g., MLflow, Weights & Biases, Kubeflow, or similar)
Solid understanding of modern NLP, computer vision, and/or reinforcement learning techniques
Strong ability to move fast without sacrificing rigor; you know when to prototype and when to productionize
Excellent communication skills with the ability to clearly present experimental results to both technical and non-technical stakeholders.

Staff Machine Learning Engineer, AI Research

Key skills