Top Tech Jobs & Startup Jobs

Design and build large-scale mining solutions to find challenging driving scenarios, improve learned model performance, and support continuous learning. Collaborate with ML infrastructure to enable iterative mining, error analysis, training and deployment. Research uncertainty and informativeness metrics, run deep learning experiments, provide statistical insight on model uncertainty and robustness, write technical reports, and mentor junior researchers.

Develop and integrate motion-planning and decision-making algorithms for autonomous driving. Build robust, scalable C++ code and CI/CD pipelines, process and analyze vehicle field data, optimize large data pipelines, lead feature development, and mentor junior engineers.

Design, implement, and test onboard and offboard localization software pipelines for autonomous vehicles. Develop sensor processing, fusion, and SLAM for deployment, perform code reviews, mentor junior engineers, and coordinate APIs with mapping and cross-functional teams (perception, planning, controls).

Design and implement C++ software enabling remote human operator control and low-latency sensor/semantic data streaming for autonomous vehicles. Build integration and simulation test suites, apply test-driven development, support on-road testing, collaborate with cross-functional teams, and develop RVA agent training tools and scenarios.

The role involves leading the ML Acceleration team, driving strategies for optimizing ML model training, recruiting and mentoring engineers, and collaborating across teams.

As a Machine Learning Systems Engineer, you will optimize distributed training systems, enhance performance profiling, and develop high-performance GPUs for machine learning workloads.

The ML Systems Engineer will optimize distributed training systems, develop high-performance GPU kernels, and ensure efficient data pipelines for machine learning models.

Lead improvements to decision-making, motion planning, and controls software quality for driverless vehicles. Implement validation, diagnostics, static analysis, compiler optimizations, and eliminate runtime allocations. Develop and deploy agentic AI tools to enforce design and validation principles, collaborate across subsystems, and present results to reduce failures before commercial launch.

Build and maintain internal developer platforms, CI/CD pipelines, build systems, and backend services to improve reliability, scalability, performance, and observability. Partner with engineering teams to optimize cloud costs, contribute to architecture and roadmaps, monitor system health, and resolve issues to enable faster, higher-quality shipping to production.

The Senior Software Engineer will own the CDE platform, architect AI agents for ML workflows, and collaborate with ML Engineers to enhance productivity.