Teams at May Mobility

Leads full-stack and infrastructure engineering teams responsible for simulation platform architecture, cloud infrastructure, container orchestration, databases, APIs, frontend tools, reliability, observability, and cost efficiency. Owns the strategic roadmap for scenario management, run orchestration, taxonomy, and results analytics. Recruits and mentors engineers, partners with simulation teams, manages integration interfaces, and delivers scalable infrastructure for massive parallel batch simulations.

Design, operate, and scale ML data and training pipelines for autonomous driving. Build distributed training, orchestration, CI/CD and infrastructure-as-code; maintain data/metadata stores and tooling to support large-scale model training and evaluation across cloud and cluster environments.

Design, train, and evaluate advanced machine learning models for autonomous driving, simulation, and ML platforms. Lead small cross-functional engineering teams, establish data and experiment practices for petabyte-scale training, address long-tail model failures, and work with multimodal datasets exceeding 100TB. The role requires commercial robotics experience, mentoring capability, expertise in VLA or generative world models, Python/PyTorch, sensor processing, and potentially edge deployment.