Jobs at Gritt Robotics

Own end-to-end hardware test strategy for robotic systems, including functional, environmental, vibration, dust, heat, and life-cycle testing. Build fixtures and automation, define acceptance criteria, investigate failures and field incidents, perform root-cause analysis, enforce safety procedures, and produce traceable, data-driven test reports for components, subassemblies, and integrated robots.

The Robotics System Test Engineer will lead test strategies for robotic systems, perform testing in outdoor environments, and ensure safety and reliability for autonomous operations.

Develop and evaluate robot perception components on real-world data: build datasets and evaluation harnesses, run experiments, integrate modules into the live robot stack, and demo on hardware. Work spans pose estimation, VLMs/VLAs, 3D reconstruction, object detection, and multimodal learning; opportunity to publish and attend conferences.

Intern will train and deploy robot policies (imitation learning, RL, or fine-tuned foundation models), build data collection and evaluation pipelines, run ablations, and iterate on real-hardware performance. Work includes pose estimation, vision-language and multimodal methods, 3D reconstruction, object detection, and testing policies on office robots.

Architect and develop scalable, high-performance robotics software and frameworks for real-world deployments. Optimize system performance, integrate multi-modal sensors, manage real-time data, and contribute across the autonomy stack using simulation and visualization tools.

Intern will build and operate training, data, and evaluation infrastructure: manage GPU clusters, orchestration, and large-scale data pipelines; instrument, monitor, and harden systems; test on robots; and deliver an end-to-end scoped project with mentorship. Opportunities to publish and attend conferences for qualifying interns.

Design, build, and maintain full-stack cloud applications and real-time frontends to monitor and control robot fleets. Implement scalable ingestion, storage, streaming, and analytics for high-volume telemetry and video. Develop agentic/LLM-based tooling, improve reliability and observability, debug field issues, and ship production software from prototype to deployed systems on construction sites.

Seeking a Perception ML Engineer to develop and deploy advanced perception models for robotics, focusing on manipulation and navigation in dynamic environments.

The Mechanical Engineer will integrate robotic systems onto heavy vehicles, focusing on design, environmental resilience, and field deployment for various construction tasks.

The ML & Cloud Infrastructure Engineer will develop scalable AI and data pipelines, deploy monitoring and CI/CD pipelines, and optimize performance for cloud-based ML applications.

The Planning & Controls Engineer will develop and deploy planning and control systems for robotics in outdoor environments, working on trajectory generation and real-time control techniques.

Lead mechanical integration for robotic platforms onto heavy mobile bases. Design rugged systems for outdoor tasks, manage integration of payloads, and ensure reliability in diverse environments.

Own day-to-day accounting and finance operations: month-end close, tax filings, financial statements, AR/AP, budgeting and forecasting, investor/audit support, internal controls, and finance systems automation for an early-stage robotics and AI startup.

Manage sourcing and purchasing of mechanical components, sensors, and compute; de-risk long-lead items; maintain POs, inventory, and BOMs; manage vendors and costs; ensure parts arrive on time and engineers aren’t blocked. Operate the in-house BOM/inventory/PO database and feed sourcing feedback into vendor selection and purchasing processes.

Develop and deploy state-of-the-art physical AI for outdoor robotics, focusing on perception, planning, control, simulation-driven training, multi-sensor integration, data pipelines, and GPU-optimized training and inference.

Lead development of scalable simulation and training infrastructure for robot learning. Implement high-fidelity physics, handle multimodal sensor data, build tools for large-scale RL/imitation learning, and optimize software for GPU parallelization. Help shape AI capabilities and grow into leadership roles.