Data Scientist – MLOps (New Grad) – Weights & Biases
Weights & Biases (W&B) is the leading MLOps platform used by 1,000+ AI companies and research labs — including OpenAI, Google DeepMind, Samsung, and 30% of Kaggle's top competitors — to track experiments, visualize model metrics, and manage ML models in production. W&B's platform has logged 500+ billion model training runs and is the de facto standard for ML experiment tracking. As AI development accelerates, the need for rigorous ML engineering discipline — experiment reproducibility, model versioning, and production monitoring — has never been greater. We are hiring New Grad Data Scientists in San Francisco to advance W&B's ML research and develop the best practices for the global ML community.
Responsibilities
- Conduct ML research benchmarking W&B's platform against experiment tracking competitors — designing rigorous experiments measuring training run reproducibility and hyperparameter search efficiency
- Build W&B Weave LLM evaluation datasets and benchmarks helping AI teams measure LLM application quality, hallucination rates, and prompt effectiveness
- Develop reference ML training pipelines (CV, NLP, RL) demonstrating W&B's best practices for experiment tracking, distributed training, and model registry workflows
- Analyze W&B platform usage data to identify patterns in how AI teams run experiments — surfacing insights for W&B's product team to improve the ML developer experience
- Build W&B Reports and dashboards demonstrating state-of-the-art results on benchmark tasks (ImageNet, GLUE, HumanEval) using W&B-integrated training frameworks
- Contribute to W&B's technical blog, research papers, and open-source example repositories used by 1M+ ML practitioners
Requirements
- Bachelor's or Master's degree in Machine Learning, Statistics, or Computer Science
- Hands-on ML experience with PyTorch or JAX — training and fine-tuning neural networks
- Familiarity with W&B, MLflow, or similar experiment tracking tools
- Strong Python skills for ML pipeline development and data analysis
- Genuine passion for ML engineering best practices and the developer experience of building AI systems
Benefits
- Competitive salary with W&B equity (Series C startup)
- W&B Teams plan access and GPU compute credits for ML research
- Medical, dental, and vision benefits
- 401(k) with W&B contribution
- San Francisco hybrid office with W&B's ML-native, research-forward culture