Teams at 42dot

Integrate and deploy machine learning models onto onboard automotive compute systems. Optimize inference for latency, throughput, memory, and power; build model deployment, conversion, validation, and rollout pipelines; diagnose runtime failures and resource contention; define model-to-system interfaces; and collaborate with autonomy ML teams to deliver reliable real-time vehicle execution.

Integrate and optimize machine-learning autonomy models for real-time execution on embedded automotive hardware. Build model deployment pipelines, improve inference latency, throughput, memory, and power usage, diagnose on-vehicle runtime failures, and define interfaces between ML models and onboard software. Collaborate with ML autonomy teams to translate deployment constraints into reliable vehicle-ready systems.

Lead development and production deployment of end-to-end driving, VLA, and world-model systems for L4 autonomous vehicles. Define scalable autonomy architectures, adapt foundation models, improve training and evaluation efficiency, and drive technical initiatives from concept to deployment. The role also includes mentoring engineers, shaping technical direction, and contributing to the founding autonomy team.