Top Tech Jobs & Startup Jobs

Build and operate a scalable model API platform for data ingestion, fine-tuning, evaluation, low-latency inference, and deployment. Design reliable multi-tenant infrastructure with rate limiting, isolation, observability, versioning, and SLOs. Collaborate with researchers and external partners to turn evolving AI capabilities into intuitive developer tools, APIs, and documentation. The role emphasizes production software engineering, backend systems, infrastructure, and model serving rather than robotics research or customer success.

Build and own a scalable API platform that enables external organizations to upload data, fine-tune and evaluate AI models, and run low-latency inference for robotics applications. Responsibilities include designing multi-tenant infrastructure, APIs, deployment integrations, observability, reliability, and developer tooling. The role requires strong backend and systems engineering skills, Python expertise, production machine learning familiarity, and end-to-end ownership of ambiguous platform problems.

Owns end-to-end performance for a portfolio of AI annotation programs, partnering with researchers to define requirements, rubrics, quality standards, and delivery operations. Drives continuous improvement, scalable solutions, and operational efficiency while coordinating technical and nontechnical stakeholders, vendors, and remote teams. Requires strong systems thinking, communication, organization, and understanding of model training and evaluation. Experience with ML annotations, robotics, multimodal data, or coding agents is advantageous.

Build internal fullstack products and human-robot interfaces for operating, monitoring, debugging, and deploying robots. Responsibilities include realtime video and robot-state systems, safety-focused UX, on-device and cloud architecture, fleet versioning, observability, workflow measurement, and direct user-driven product development. The role partners with researchers, operators, and roboticists across lab, warehouse, and field environments.

Design, prototype, test, and integrate robotic arm and mobile platform subsystems spanning mechanical, electrical, embedded, and software disciplines. Responsibilities include hardware bring-up, assembly, calibration, reliability testing, data-driven debugging, and root-cause analysis. The intern will own subsystem development from CAD and PCB work through bench testing while supporting robotics data collection and model training operations.

Own high-volume recruiting for Robot Operators and related site-based roles in San Francisco. Build candidate pipelines through job boards, referrals, partnerships, events, schools, and community organizations; conduct efficient initial screens; manage funnel throughput and candidate experience; track conversion metrics; partner with operations leaders; and improve repeatable recruiting processes and automation.

Design and implement embedded firmware for data-collection devices, including embedded Linux bring-up, sensor drivers, BSP customization, kernel debugging, hardware/software integration, performance tuning, and power optimization. Collaborate with research, hardware, software, and fleet teams to develop first-of-their-kind devices, debug complex concurrency and interface issues, and bring products from concept to production.

Build and deploy internal AI tools, agents, integrations, automation, and workflows that improve productivity across engineering, research, operations, and recruiting. Drive adoption through enablement, documentation, training, and best practices. Evaluate AI tools, measure impact, and ensure secure data access and permissions while partnering across technical and business teams.

Own and optimize infrastructure for large-scale ML training and inference. Build JAX-based pipelines, manage GPU and TPU workloads, improve distributed performance, and create systems for scheduling, monitoring, checkpointing, debugging, and reproducible experimentation. Partner with researchers to translate modeling ideas into production training runs and contribute to core training code supporting new architectures, modalities, and evaluation metrics.

This role involves executing cross-functional projects, driving partnerships, managing deployment efforts, and supporting communications with investors and external parties.