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

Develop and maintain production C++ software running on autonomous vehicles. Integrate sensors and electromechanical systems, diagnose faults using data traces and lab rigs, deploy updates to field vehicles, enable new mission features, build fleet software tooling, and verify hardware-software integration on physical vehicles.

Lead a research program focused on continual learning, model adaptation, self-improvement, and memory. Develop experiments, collaborate with ML and software engineers, and transition research into production features. The role requires applied research experience, post-training expertise for large models, strong Python and deep learning skills, distributed training experience, and publications at leading venues. This is an on-site senior individual contributor position in a fast-paced startup environment.

Develop and deploy proprietary machine learning models for a software product, managing data preparation, experimentation, production deployment, monitoring, and refinement. Build neural networks and learned representations, improve experiment tooling, collaborate with product teams, and write reliable Python. The role requires production machine learning experience, advanced modeling skills, and familiarity with transformers, large language models, and graph-based architectures. Bonus experience includes world models, robotics, simulation, cloud ML services, backend development, SQL, CAD, and geometry computing.

Lead the identification, assessment, vetting, and integration of machining and additive manufacturing vendors nationwide. Manage vendor relationships, evaluate quality and capacity, expand the production network, interpret engineering drawings, assess manufacturability, and visit vendor sites to observe operations and build partnerships.

Lead the design and delivery of production AI infrastructure, including context-aware data layers, configurable autonomous agents, event-driven automations, model-agnostic inference, access controls, and workflow governance. Contribute approximately 30% of the time to full-stack product engineering and on-call support. Partner with founders on architecture, technical direction, and engineering standards.

Build AI-driven, full-stack customer solutions from prototypes through production deployment. Develop integrations with business systems, design LLM prompts, lead technical projects, participate in sales conversations, present system designs, and translate customer feedback into reusable platform components and product recommendations. The role requires close customer collaboration, production-grade engineering, clear communication, and ownership in a fast-paced startup environment.

Lead the product function by owning the roadmap, writing specifications, managing delivery, guiding product design, and measuring release outcomes. Grow a two-sided platform, incorporate client feedback, and address enterprise security, privacy, and compliance needs. The role also involves recruiting and developing a product team while partnering with engineering, go-to-market leaders, and the CEO.

Lead applied machine learning research from ideation through production deployment. Develop predictive, optimization, and language-model-based systems; curate specialized datasets; retrain models as conditions change; and conduct large-scale experiments and data engineering. The role requires strong Python skills, quantitative modeling experience, and a doctoral degree in a relevant discipline.

Own backend development and operation of AWS cloud services handling data from remote equipment. Design APIs, databases, schemas, and distributed systems using Python and TypeScript; improve mature multi-repository codebases; troubleshoot production workloads; and enhance resilience, scalability, observability, and handling of incomplete data. Collaborate with management and frontend engineers while supporting operational applications and physical-world systems.

Lead production AI engineering for a software product, owning AI feature design, model integration, data flows, evaluation, deployment, monitoring, and support. Build automated quality checks, optimize latency and costs, investigate model, data, and interface issues, and release weekly. Establish long-term architecture and serve as the team’s model-related technical expert.