Top Tech Jobs & Startup Jobs

Architect and develop high-fidelity multi-robot simulation environments and fleet orchestration systems. Build realistic physics, perception, communications, task allocation, planning, scenario generation, domain randomization, metrics, and experiment infrastructure. Calibrate simulations against real-world telemetry, automate regression benchmarks, and collaborate with robotics, infrastructure, DevOps, and field teams to deliver reliable, scalable, field-ready systems.

Design, implement, and deploy motion planning, trajectory generation, and control algorithms for wheeled, legged, and humanoid robots. Develop navigation and optimization approaches, integrate planning with perception and control, build testing pipelines, evaluate algorithms in simulation and field deployments, analyze telemetry, and improve autonomy performance in challenging environments. The role requires strong C++ and Python skills, robotics middleware experience, and familiarity with sensors and planning libraries.

Integrate, debug, deploy, and support autonomous wheeled robotic systems across federal and commercial programs. Build integration utilities and automated testing tools, improve software reliability, analyze system performance, collaborate across hardware and software domains, support customers in the field, and translate operational feedback into technical solutions. The role is fully onsite in Irvine, California and requires U.S. Person eligibility.

Develop and maintain AR/VR applications for supervising, manipulating, and controlling humanoid robots. Design spatial interaction workflows, integrate user interfaces with robotic perception and control systems, and deploy reliable applications on real robots. Prototype and evaluate interfaces through user studies, optimize distributed systems across headsets and robotic hardware, and collaborate with robotics, hardware, and software teams on testing, monitoring, and field support.

Develop and own independent safety systems for wheeled, legged, and humanoid robots. Responsibilities include runtime monitoring, collision avoidance, operating-envelope enforcement, fallback behaviors, watchdogs, hazard analysis, safety cases, and verification through simulation, hardware-in-the-loop testing, and field trials. The role collaborates with planning, control, locomotion, and integration teams to ensure autonomous systems operate safely around people, equipment, and terrain.

Develop and deploy whole-body manipulation and autonomy capabilities for humanoid and quadruped robots. Build model-based and learning-based policies, integrate perception and locomotion, adapt vision-language-action models, and design safe grasping and force-control strategies. Create simulation and evaluation pipelines, address sim-to-real challenges, and validate behaviors on physical robots in laboratory and field environments.

Designs and operates AWS and Kubernetes infrastructure, CI/CD systems, GitOps workflows, and internal developer platforms. The role improves reliability, scalability, observability, deployment velocity, and operational maturity across production systems and data pipelines. Responsibilities include infrastructure as code, monitoring, autoscaling, incident response, debugging across application and infrastructure layers, and partnering with engineering teams on platform standards and developer productivity.

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.