Robotics Platform Engineer (AI)

Location: San Mateo, United States; hybrid

Company

hirify.global builds data collection systems, operational capabilities, an exabyte-scale data warehouse, and software tooling that support the development of general-purpose robots and foundation models.

What you will do

• Deploy and run learned robot policies, including VLAs, imitation learning, and DAgger workflows, on physical hardware.
• Design evaluation frameworks for robotic policy performance, including metrics, result aggregation, and partner-facing reporting.
• Build product features for configuring, launching, and monitoring model deployments and evaluation runs.
• Build orchestration systems that schedule deployments and evaluations across robot stations.
• Integrate LLMs and foundation models into robot workflows for task specification, error recovery, and high-level planning.
• Containerize robot-side software and connect on-robot systems with cloud compute, storage, and orchestration.

Requirements

• Bachelor’s degree or equivalent experience in Computer Science, Robotics, or a related field.
• 3+ years of industry software engineering experience with exposure to robotics or ML deployment.
• Strong Python skills and comfort working in Linux environments.
• Experience deploying ML models to real systems, rather than only training them.
• Familiarity with Docker and reproducible deployment workflows.

Nice to have

• Experience deploying imitation learning, reinforcement learning, or VLA policies on physical robots.
• Experience building evaluation or benchmarking infrastructure for robotic systems or ML models.
• Experience with DAgger or other interactive data collection and policy refinement loops.
• Experience with ROS or ROS2 and robotics middleware integration.
• Experience building product features or partner-facing tools on top of technical infrastructure.
• Comfort working in 0→1 environments.
• Passion for robotics and alignment with the mission.

Culture & Benefits

• Fast-paced work focused on robotics, learned policies, and real-world hardware.
• Work in an environment building new systems and products from 0→1.