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As an ML Infra Engineer, you will optimize and manage training systems for large-scale models, collaborating with researchers and engineers on infrastructure and experiment scalability.

The Build & Release Engineer will manage infrastructure and developer platforms, focusing on CI/CD systems, build reliability, and Kubernetes services to improve developer productivity and operational efficiency.

Design and rapidly fabricate mechanical, electrical, and sensor prototypes for robotics research. Collaborate with researchers and engineers to translate experiments into CAD, PCBs, wiring, sensor/actuator integration, and validated end-to-end prototype systems. Build fixtures, test rigs, and tooling; perform hands-on assembly, debugging, and documentation to support real-world deployments.

Build and own the manufacturing and hardware-test software platform: convert procedures into repeatable workflows, implement local station software, integrate inventory and firmware management, ensure observability and resilience, automate tests, and debug across cloud, Linux stations, embedded devices, and test equipment.

Lead sourcing and supplier execution for the full robot BOM, build resilient global supply base, drive cost and should-cost improvements, manage CM transitions and NPI ramps, and ensure material continuity using MRP/ERP systems while partnering with engineering and executive leadership on supply risk and capacity planning.

The role involves integrating robot platforms into a fleet, defining and executing acceptance criteria, maintaining performance, and collaborating cross-functionally to improve system reliability.

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

Design and implement control algorithms for robotics, develop real-time control loops, and debug complex hardware/software issues while collaborating with cross-functional teams.

As a Forward Deployed Robotics Engineer, you'll manage robotic systems' technical issues in real-world settings, performing diagnostics, collaborating with teams, and analyzing data for resolution and improvements.

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.