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Build full-stack features and realtime web interfaces for the Autonomous Traffic Control platform. Develop frontend UIs (maps, telemetry) and backend services/APIs, integrate streaming feeds, and ship reliable, operational tools used by controllers, fleet operators, and drivers.

Develop and train conditioned policies and MARL systems to simulate realistic driving behaviors, implement safety-constrained RL algorithms, design rewards and evaluation metrics, optimize large-scale training pipelines, advance neural architectures for long-horizon planning and spatial reasoning, and integrate research models with production simulation and planning teams.

The Senior ML/RL Engineer will develop behavior models, implement RL algorithms focusing on safety, design reward functions and optimize training environments while collaborating across teams.

Responsible for designing and maintaining build and release pipelines for autonomous driving software, optimizing development workflows, and collaborating with teams.

The role involves designing and developing machine learning infrastructure for annotation, evaluation, and training models, focusing on scalable systems and efficient data workflows.

The role involves exploring ideas in deep learning, managing machine learning project life cycles, and collaborating on autonomous driving technology.