Research Scientist - Machine Learning

Responsibilities

• Collaborate with senior researchers, residents, engineers, and physicists to derive the theory of new probabilistic models and their learning rules, including energy-based models and diffusion models.
• Scale up experimentation infrastructure and optimize over the design space of models.
• Implement, visualize, and evaluate new architectures, training algorithms, and benchmarks.
• Publish papers, contribute to open source, and communicate design insights to our hardware team.
• Create production models for domain experts using customer data.

Required Qualifications

• Experience in scientific Python and at least one deep learning framework (PyTorch, JAX, TensorFlow, Keras)
• Extremely strong foundations in probability and linear algebra
• Familiarity with deep learning theory and literature, including theory of over-parameterization and scaling laws
• Publications in top ML conferences (NeurIPS, ICML, ICLR, CVPR)
• Experience training high-performance models, including familiarity with infrastructure (Slurm, Ray, Weights & Biases)
• Experience deploying models, including familiarity with infrastructure (Ray, AWS, ONNX)

Preferred Qualifications

• Experience designing probabilistic graphical models (PGM)
• Experience training energy-based models (EBMs) or diffusion models
• Experience with numerical methods in diffeq solvers
• Experience with message passing or training graph neural networks (GNNs)
• Strong theoretical background in information geometry
• Strong theoretical background in random matrix theory
• Strong grasp of computational Bayesian methods, including MCMC sampling methods and variational inference