You will lead a team of engineers to define and execute the technical strategy for accelerating ML model training and deployment. This involves designing scalable solutions and collaborating across departments to optimize performance for autonomous vehicle services.
Lead a diverse team of engineers to develop and maintain large-scale AI model and software evaluation frameworks for autonomous vehicles. Drive innovation in data warehousing and analysis while ensuring rigorous validation of ML model performance and safety.
The Perception Engineer will design, develop, and implement features in the codebase while architecting solutions for autonomous vehicle technology. They are responsible for training and deploying AI models, analyzing performance, and iterating on solutions to enhance system capabilities.
Lead the architecture and scaling of a modern frontend ecosystem to create critical web visualization tooling for machine learning engineers. Drive cross-team standards and collaborate with UX and data teams to improve autonomous vehicle system feedback loops.
Improve the quality, reliability, and robustness of decision making, motion planning, and controls subsystems for autonomous vehicles. Develop and deploy agentic AI agents to streamline design and validation principles while enhancing software quality assessments.
Lead the technical strategy and architectural design of motion planning and trajectory optimization algorithms for autonomous vehicles. Coordinate with cross-functional teams to implement safety-critical software and mentor engineering staff.
Technically oversee the design and deployment of an Embodied AI and LLM-based monitoring framework for off-board scenario understanding and root cause analysis. Lead the creation of an evaluation layer to assess autonomous vehicle driving intelligence and safety using fleet data and drive logs.
Lead and grow a high-performing team of full stack engineers to build AI data applications for ML dataset generation and search. Define the technical vision for metadata storage and AI adoption strategies to accelerate development cycles.
Build and enhance Agentic AI tooling and high-scale ML infrastructure using Kubernetes to empower developer productivity. Partner with researchers to optimize distributed training jobs and resolve system-level bottlenecks to maximize GPU utilization.
Lead the development of evaluation frameworks and metrics to validate the performance and safety of autonomous robotaxis. Collaborate with engineering teams to identify edge cases and ensure the autonomy stack meets rigorous safety standards for commercial launch.
Develop and implement performance-critical motion control and trajectory optimization algorithms for autonomous robotaxis. Collaborate with cross-functional teams to build integrated solutions and mentor junior engineers on product-focused development.
Lead the AV Behavior Understanding and Evaluation team to define standards for safe and assertive autonomous driving. Drive the design, evaluation, and scaling of system behaviors using data-driven insights from simulation and real-world testing.
Develop large-scale solutions for mining challenging driving scenarios to improve autonomous driving model performance. Collaborate with infrastructure teams to create continuous learning workflows and provide statistical insights on model robustness.
Design and implement software pipelines for autonomous vehicle localization systems and collaborate with mapping, perception, and motion planning teams. Provide technical leadership through code reviews and guidance for junior team members to ensure software quality and efficiency.
Develop and integrate planning algorithms for autonomous driving while building a robust and scalable codebase. Interface with system components via CI/CD pipelines and provide mentorship to junior team members.
Develop and train ML pipelines for multimodal sensor data to identify critical driving scenarios and edge cases. Build scalable data preprocessing pipelines and monitor production model health and performance.
Drive complex technical programs and model release cycles for driverless robotaxi capabilities. Lead cross-functional integration across Prediction and ML Planning teams while managing technical risks and roadmaps.
Design and assess AV behavior competencies using data-driven assessment from on-road and simulation modalities. Develop metrics, automate result analysis, and provide strategic insights to improve AV performance and safety.
Define and execute motion planning and prediction projects to improve autonomous vehicle navigation in complex traffic. Design and deploy ML behavior models using generative AI and reinforcement learning onto vehicle fleets.
Define and execute motion planning and prediction projects to improve autonomous vehicle navigation in complex traffic. Design, implement, and deploy ML behavior models using generative AI and reinforcement learning onto vehicle fleets.
Build and lead a new AI Data Engine team to accelerate ML dataset generation and optimize the data warehouse stack. Define the technical strategy for storing metrics and querying large datasets to support driverless technology development.
Build and lead the AI Data Engine team to accelerate ML dataset generation and optimize the data warehouse stack. Define the technical strategy for storing metrics and querying large datasets to support driverless technology development.
The Principal Engineer Tech Lead Manager will build and lead a new Machine Learning Acceleration team, driving the strategy and execution of initiatives to accelerate ML model training. This role involves providing technical guidance and fostering a culture of innovation and collaboration within the team.
You will be responsible for training, evaluation, and deployment of ML-based models for scene understanding and behavior prediction. This includes designing experiments, implementing metrics, and releasing model updates.
