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Architect and build the robotics platform enabling FieldAI autonomy systems to run reliably across diverse robots and hardware. Define versioned APIs, robot capability and intent models, middleware, runtime infrastructure, and high-performance data flows. Own latency, throughput, reliability, profiling, tracing, simulation, cloud connectivity, and deployment on physical hardware. Collaborate across autonomy, hardware, infrastructure, and deployment teams while setting technical direction and mentoring engineers.

Develop and deploy learned manipulation models for humanoid robots, combining perception, planning, control, reinforcement learning, and imitation learning. Build robotics foundation models, scalable training and evaluation pipelines, data systems, and experimental infrastructure. Test systems on real hardware and in field environments, collaborating across research, engineering, and field teams to deliver reliable, generalizable robotic autonomy.

Conduct research on large-scale learned humanoid manipulation and robotics foundation models. Develop architectures, learning objectives, reinforcement and imitation learning methods, multimodal behavior models, and cross-embodiment transfer techniques. Design rigorous benchmarks and real-world experiments, train and deploy models on physical humanoid robots, analyze failures, and translate findings into scalable systems. Provide technical leadership, mentor colleagues, and communicate results through reviews, publications, presentations, and open-source work.

Leads large, complex customer technology programs from scoping through deployment and operational handoff. Serves as the primary executive customer interface, translates business objectives into program plans, coordinates Technical Project Managers and cross-functional teams, manages budgets, risks, dependencies, governance, and contractual commitments, and drives delivery across product, engineering, solutions, and operations. The role also supports enterprise-scale expansion, stakeholder alignment, and continuous improvement of program-management practices.

Lead development of testing and validation for field-ready AI and robotics systems. Design automated test frameworks, validate reliability and risk-aware behavior, collaborate with engineering to improve models through real-world deployments, and ensure robust, production-quality software for robots operating in the field.

Design, build, and maintain CI/CD, build systems, monitoring, and deployment frameworks; automate infrastructure provisioning; ensure reliability, observability, security, and high availability; collaborate with developers and DevOps to optimize systems and troubleshoot production issues.

Lead the design, implementation, and deployment of control systems for autonomous vehicles, focusing on multi-agent collaboration and real-world applications.

The Embedded Systems Engineer designs and integrates embedded computing systems for robotic platforms, collaborating with teams and ensuring performance in field conditions.

As a Mechanical Engineer, you will design, implement, and validate mechanical systems for robotic platforms, ensuring reliability in real-world environments. Responsibilities include CAD modeling, thermal management, materials selection, and collaboration with cross-functional teams.

The Robot Autonomy Engineer at Field AI supports deployment, integration, and performance of autonomous robotic systems, collaborating with customers and teams to enhance reliability and drive adoption.