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Build facility-scale simulated worlds and pipelines for loco-manipulation: real-to-sim reconstruction, procedural scene generation, synthetic data for joint base+arm policies, photorealistic rendering to close sim-to-real gaps, and evaluation benchmarks. Collaborate with AI Research and Data Ops to prioritize simulated data and feed real-world failure modes into simulation.

This role involves leading business operations, addressing operational bottlenecks, supporting customer deployments, and managing various analytical tasks in a high-growth startup.

Deploy and maintain on-site robotics systems: network bring-up, inference server provisioning, incident response, cross-stack debugging (Linux, containers, GPUs, hardware), customer coordination, runbook and automation creation, and reporting issues back to engineering.

Manage interview scheduling and logistics, maintain ATS accuracy, coordinate candidate travel and onboarding, follow up for timely feedback, and support HR ops to ensure a smooth hiring experience.

Design and build data systems for robotic intelligent workflows, focusing on automation, signal extraction from multimodal data, and ML model evaluation.

Accepting applications for a limited number of research-focused internships in robotics. Seeking candidates passionate about the intersection of geometry and robotics; values problem-solving, grit, technical rigor, and diversity. Open to applicants at all levels.

Annotate data on robot tasks to improve AI performance. Maintain data integrity, apply guidelines, and use pre-annotation tools for quality checks.

Lead hardware builds, diagnostics, validation, and quality control for robotic systems. Assemble and troubleshoot electro-mechanical assemblies, develop test plans and fixtures, drive root cause analysis, maintain SOPs and metrics, support deployments, and mentor junior technicians.

The role involves designing and maintaining large-scale ML infrastructure, optimizing distributed training systems, and enhancing computing performance for model training.

Own continuous learning for deployed robot fleets: build pipelines to convert deployment data into fine-tuning and policy updates, mine multimodal fleet data for failures and drift, apply RL and reward modeling, create automated real-time monitoring and evaluation, and partner across teams to ship measurable improvements to production robots.