Top Tech Jobs & Startup Jobs in San Francisco Bay Area, CA

Build and operate a reliable ML platform for large-scale distributed GPU training. Responsibilities include Kubernetes infrastructure, developer-facing CLIs and SDKs, AWS infrastructure, data and artifact management, experiment tracking, model registries, CI/CD, observability, documentation, security guardrails, and platform adoption. The role partners closely with researchers and engineers to improve training speed, reliability, usability, and cost efficiency for autonomous transportation systems.

Design and develop machine-learning approaches for vehicle control, including learned vehicle dynamics models, adaptive controllers, data pipelines, evaluation metrics, and simulation tooling. Integrate learned models into safety-critical closed-loop systems and validate algorithms through offline experimentation, simulation, and on-vehicle testing. Collaborate with engineers and research scientists to combine classical control with AI for scalable self-driving technologies.

Build and deploy internal AI tooling, agents, integrations, and workflows that improve productivity across engineering, research, and business teams. Evaluate emerging AI tools and models, develop observability and responsible-use guardrails, and partner with stakeholders to prototype and scale solutions. Drive adoption through documentation, enablement, and hands-on support while integrating AI into autonomy, simulation, ML training, and business workflows.

Develop signal processing algorithms for camera, LiDAR, radar, and other autonomous-driving sensor data. Design acquisition, fusion, filtering, and learning-based methods; optimize them for CPU, GPU, DSP, and specialized accelerators; and collaborate with autonomy and hardware teams to deliver reliable, low-latency production systems. The role also supports experimental validation and potential research publications.

Lead software quality for autonomous vehicle planner architecture, developing fault-tolerant systems and improving reliability across motion planning and onboard software teams. Design motion planning software and decision-making algorithms for complex traffic scenarios, while managing experimentation and data-generation pipelines. Collaborate with multidisciplinary engineering and research teams to deliver safe, production-quality self-driving technology.

Decompose product guidance into use cases, functional requirements, and system architectures for hardware development. Collaborate with multidisciplinary engineers throughout the product lifecycle, author systems engineering artifacts, qualify work products, manage technical risks, and guide safety, security, and quality attribute decisions. Apply systems engineering analysis methods, requirements management, standards decomposition, and testing practices to support reliable autonomous transportation hardware.

The role involves building standardized training frameworks, profiling model performance, collaborating with teams, and improving related tooling.

Lead automotive partnerships, negotiate agreements, and execute strategy aligned with company goals while collaborating across engineering, product, and finance teams.

The Applied Data Scientist will analyze performance data of Waabi's autonomy systems, develop automated dashboards, and collaborate across teams to drive insights for performance improvements.

As a Research Engineer in Sensor Simulation, you will develop multi-sensor rendering systems for self-driving technologies, collaborate with scientists and engineers, and work on innovative solutions using machine learning and neural rendering techniques.