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As an AI/ML Engineer, you will develop multimodal AI models, focusing on computer vision and agentic capabilities. Responsibilities include data curation, model training, evaluation, and deployment while working with real robotic systems.

The MLOps Engineer will design and maintain infrastructure for machine learning systems in robotics, focusing on deployment, monitoring, and optimization of ML workflows.

As a DevOps Engineer, you will improve CI/CD pipelines, manage containerized infrastructure, ensure deployment reliability, and troubleshoot production issues.

The Software Engineer focuses on building and maintaining development environments and tooling infrastructure to support engineering teams, improving developer velocity and code quality across projects.

The Forward Deployed Engineer will manage deployment, integration, and operational performance of autonomous robotic systems, collaborating with teams and ensuring mission-critical functionalities.

The Sensor Systems Engineer designs, integrates, and validates sensors for robotic platforms, ensuring accurate data collection in various environments. Responsibilities include system architecture, calibration, performance evaluation, and collaboration across teams.

Develop and optimize large-scale point-cloud processing pipelines (registration, denoising, segmentation, primitive extraction); design efficient 3D algorithms focused on memory and runtime; implement production-grade computational geometry and linear algebra in C++ and Python; solve reconstruction challenges (loop closure, global consistency, multi-view fusion); evaluate and integrate emerging 3D vision methods and work with platform teams for scalable deployment.

The Robotics Autonomy Engineer designs and implements motion planning and control algorithms for robotic systems, ensuring precise navigation and integration across autonomy layers, while analyzing performance and improving field deployment.

Own infrastructure for humanoid robotics research, including CI/CD, containerized builds, simulation and hardware-in-the-loop testing, Airflow data pipelines, dataset registries, and AWS infrastructure managed as code. Operate Kubernetes, ECS, Batch, RDS, S3, networking, secrets, observability, and access controls. Establish compliance, reproducibility, lifecycle policies, documentation, and runbooks while supporting researchers and coordinating infrastructure reuse with platform and cloud teams.

Integrate humanoid hardware and software subsystems into a cohesive, deployable robot stack. Own bring-up, calibration, diagnostics, validation, and system-level debugging across perception, planning, locomotion, manipulation, controls, sensors, compute, and networking. Develop integration and regression tests, telemetry tools, dashboards, and release workflows while partnering with cross-functional teams to harden research into maintainable, field-ready modules.