Teams at PhysicsX

Lead research workstreams applying machine learning and deep learning to physics and engineering problems. Set technical direction, plan roadmaps, mentor researchers, formulate mathematical models, build predictive models for physical systems, run experiments, collaborate with engineers and customers, translate research into production code, and communicate findings through publications and industry engagements.

Develop and productionize machine learning models, libraries, and tooling for physics and engineering applications. Build scalable cloud infrastructure for distributed multi-GPU and multi-node training and inference, optimize pipelines from scheduling through GPU kernels, process large-scale datasets, and apply software engineering best practices while collaborating with research scientists and simulation engineers.

Own the development and productionization of LLM post-training capabilities. Build data, training, evaluation, reward, verifier, and deployment pipelines; improve open models for engineering and agent benchmarks; manage customer-controlled model customization across SaaS, VPC, and air-gapped environments; and define the product roadmap in partnership with research, platform, and product teams.