Jobs at FieldAI

Deploys, integrates, troubleshoots, and maintains autonomous robotic systems in customer environments. Analyzes field feedback, improves system reliability, builds monitoring and diagnostic tools, trains customer operators, and collaborates with software, hardware, AI research, product, and engineering teams to scale deployment practices.

Develop and train whole-body loco-manipulation policies for humanoid robots, deploy learned behaviors to physical hardware, improve motion retargeting and simulation fidelity, and build scalable evaluation and robot-learning infrastructure. The role bridges research and production engineering across reinforcement learning, simulation, control, perception, and hardware deployment.

Conduct research on humanoid manipulation, loco-manipulation, perception, planning, and control. Design and evaluate robotic systems in simulation or hardware, support robotics foundation-model data pipelines, and integrate manipulation data into learning frameworks. Collaborate with researchers, engineers, teleoperators, and field teams on hardware bring-up and autonomy goals. Contribute to academic publications and conference presentations.

Conduct research on humanoid manipulation, loco-manipulation, perception, planning, and control. Design and evaluate robotic systems in simulation or on hardware, support robotics foundation-model data pipelines, and collaborate on hardware integration, teleoperation, and autonomy. The internship emphasizes embodied intelligence, real-world experimentation, and publishing research in leading robotics venues.

Own failure triage and reliability improvements for deployed autonomous robots. Analyze robot behavior, logs, telemetry, and model outputs; build datasets and evaluation sets; train and fine-tune ML models; and partner with modeling, data, deployment, and robotics teams to validate and ship improvements across real-world sites.

Twelve-week internship developing robotics simulation tools for real-to-sim workflows, sensor modeling, scenario execution, log replay, regression testing, metrics, and visualization. The intern will use real robot data and field logs to evaluate simulation fidelity and investigate differences between simulated and real behavior. Responsibilities include software development in Python or C++, collaboration with simulation and autonomy engineers, documentation, and a final technical demonstration.

Execute manual and structured testing of robotic systems in lab and controlled environments. Monitor robot behavior, identify and reproduce bugs, document failures, validate fixes, perform regression testing, and improve QA procedures and checklists. Collaborate with engineering teams to resolve issues and provide real-time feedback on system performance, safety, and reliability.

Develop and deploy perception systems for autonomous robots operating in unstructured, off-road environments. Responsibilities include localization, mapping, SLAM, multi-sensor fusion, sensor integration and calibration, field testing, debugging on physical robots, performance monitoring, and production deployment. The role collaborates with autonomy, controls, platform, research, and field operations teams to deliver robust perception capabilities across multiple robotic platforms and missions.

Build and operate scalable data-platform systems for multimodal robotics and machine-learning datasets. Responsibilities include ingestion, processing, validation, labeling, dataset generation, metadata, lineage, versioning, data quality, observability, and workflow reliability. Partner with ML, robotics, infrastructure, and labeling teams to create reusable platform capabilities supporting large-scale data processing, reprocessing, backfills, training, and evaluation.

Build and automate data-flywheel systems for autonomous robots, converting large-scale robot experiences into high-quality, versioned training datasets. Responsibilities include data processing, filtering, labeling, auto-labeling, offline inference, hard-example selection, validation, reproducible dataset generation, and incremental training-data pipelines. Partner with researchers and platform teams to productionize experimental workflows and improve the cycle from robot deployment to usable ML data.

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.