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

Design and integrate vision and audio models into agent reasoning loops, optimize streaming latency for voice-to-voice interactions, and architect multimodal RAG systems to retrieve insights from videos and PDFs.

Design and implement Visual-Inertial SLAM pipelines, dynamic obstacle avoidance, and predictive path planning. Optimize state-estimation and navigation algorithms for low-compute edge deployment on robotic platforms.

Develop and evaluate high-resolution tactile sensing and dexterous manipulation systems by processing tactile and force-torque data, implementing slip-detection and compliant control, training multi-fingered grasping policies, integrating solutions into robotic platforms, and conducting experiments to validate performance.

Design and implement functional safety systems for robotic products to meet ISO standards. Develop redundant safety-rated PLC architectures, implement collision-avoidance logic, and perform comprehensive FMEA on hardware and software.

Develop multimodal intent-recognition models from gaze, gesture, and voice; implement socially aware navigation respecting personal space; design robot visual and auditory UI feedback; run user studies and rapidly prototype AI-driven voice and visual interfaces.

Design and implement AI platforms (front-end and back-end), collaborate on data preprocessing and model training, deploy AI models to production, build and maintain data pipelines, integrate AI with an Ethereum Layer 2/Web3 stack, and contribute to related company projects with potential to lead a team.

Design, test, and optimize electromechanical systems and motor control firmware. Specify and tune BLDC motors, harmonic drives, and gearboxes. Implement FOC firmware on microcontrollers and integrate industrial communications (EtherCAT, CAN). Collaborate with mechanical engineers on joint design and thermal management to improve efficiency and torque density.

Build and operate MLOps and agentic infrastructure: manage model registries, continuous training loops, and A/B testing; deploy agents as scalable Kubernetes microservices; and create observability dashboards tracking token usage, latency, and agent reasoning.

Design, orchestrate, and optimize large-scale LLM pre-training across 1,000+ GPUs. Implement 3D parallelism, manage GPU clusters (SLURM/Kubernetes), optimize InfiniBand/RDMA networking and memory, and automate checkpointing and failure recovery for long training runs.

Design and implement multi-agent pathfinding algorithms to avoid fleet deadlocks, build low-latency Robot-to-Cloud and Robot-to-Robot communication protocols, and develop cloud dashboards for fleet monitoring and teleoperation handoffs.