Top Tech Jobs & Startup Jobs

As an ML Infra Engineer, you will optimize and scale training systems, manage GPU/TPU compute, and develop JAX training pipelines, collaborating with researchers for efficient model training.

Design and implement low-latency teleoperation interfaces and real-time video/streaming systems for on-site robots. Profile and optimize across drivers, networking, compute, and storage; build reliability, tracing, and debugging tooling; collaborate with researchers, hardware engineers, and operators to deploy dependable production robotics systems.

Deploy, debug, and iterate learned policies on physical robots across perception, policy, control, and hardware. Train and tune policies, curate data, build production-quality code, set up tests and data pipelines with operators, engage partners in deployment contexts, and translate research advances into reliable, real-world robotic systems.

Engineer low-latency, high-throughput sensor and actuator pipelines, optimize system performance, and integrate complex models into real-time workflows while collaborating across teams.

Full-lifecycle technical recruiter owning searches for ML, robotics, systems, and infrastructure roles. Source passive top-tier candidates, design interview processes, advise on compensation and closing strategies, partner with founders and hiring managers, build market maps and dashboards, and steward candidate experience with data-driven sourcing and hiring approaches.

Build and operate large-scale data infrastructure for robot learning: ingest, validate, transform, and featurize multimodal data; run batch and streaming workflows; design storage and metadata systems; optimize training-time performance; manage data movement and lifecycle; implement observability and guardrails; collaborate with researchers and engineers.

Lead and scale data annotation operations from hundreds to thousands, owning throughput, quality, cost, hiring, training, tooling, vendor mix, and budget. Build multi-layer management, human-in-the-loop/autolabeling workflows, QA/rubrics, operational metrics, capacity planning, and partner with product, engineering, and research to meet SLAs and improve unit economics.

Own product roadmap for Research Operations; design and improve systems for intake, prioritization, resource allocation, execution visibility, quality management, and operational planning. Partner with engineers, project leads, Production Operations, and researchers to build tools, standardize processes, establish metrics and dashboards, remove bottlenecks, and prioritize investments that scale research execution.

The Recruiting Coordinator will schedule interviews, manage candidate pipelines, enhance recruiting workflows, support hiring managers, and maintain candidate communication.

Own delivery of company-critical research and deployment initiatives: define scope, prioritize work, allocate resources, build execution plans, direct cross-functional execution, resolve risks and bottlenecks, run stakeholder communications, and improve operational systems as the organization scales.