Lead and develop a high-performing team building and optimizing data, training, and evaluation pipelines and tools for perception machine learning. Set team priorities and technical direction, contribute production code, guide design and code reviews, and collaborate with ML engineers to improve end-to-end development workflows.
You will design and implement safety-critical motion planning algorithms to ensure autonomous vehicles execute safe trajectories and evasive maneuvers. Additionally, you will develop simulation tools and analyze fleet-wide data to validate system performance and safety metrics.
You will diagnose and resolve complex issues within the autonomous vehicle planning and controls stack by analyzing logs and simulation data. Additionally, you will develop internal tooling and automation to improve team efficiency and lead the deployment of software fixes.
Lead the architecture and development of advanced perception systems, including multi-modal foundation models and synthetic data generation. Provide technical leadership and mentorship to team members while ensuring the productionization of solutions onto autonomous vehicle fleets.
The engineer will design, evaluate, and improve complex interactions between autonomy, remote operations, and customer-facing software components. They are responsible for setting and measuring performance targets to ensure the successful launch of the Robotaxi service.
Design and build scalable data infrastructure, backend services for data discovery and lineage, and robust pipelines that process petabytes of data. Lead system design and code quality efforts, and maintain scalable data processing and access using cloud ETL technology.
Develop and implement state-of-the-art trajectory optimization algorithms to ensure safe and comfortable autonomous vehicle motion. Collaborate cross-functionally with teams in decision planning, prediction, and vehicle control to build integrated, scalable software solutions.
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.
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.
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.
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.
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.
