What are the responsibilities and job description for the Robotics & Machine Learning Systems Engineer position at Skylark Labs?
Location: Menlo Park, CA (Bay Area) • On-site • Occasional travel to test sites
Team: Robotics, Machine Learning & Sensors
Type: Full-time
About the Role
We’re looking for a Systems Engineer who lives at the intersection of robotics, machine learning, sensors, and VLA (Vision-Language-Action) models. You’ll architect, integrate, and harden multi-sensor robotic systems, then work closely with ML and autonomy teams to connect perception, reasoning, and action so robots can understand instructions, plan, and operate safely in the real world.
What You’ll Do
• Own system integration across sensors (EO/IR cameras, LiDAR, radar, audio, tactile), compute (Jetson/RTX), and platforms (UAV/UGV/static towers).
• Build reliable robotics stacks: ROS 2 nodes, real-time pipelines, health monitoring, logging/telemetry, OTA updates.
• Interface with autonomy: connect perception state estimation to planners/controllers; ensure timing, synchronization, and fail-safes.
• VLA enablement: integrate and evaluate VLA/LLM-based policies for instruction following, task decomposition, and grounding to robot actions.
• Collaborate closely with ML engineers to deploy, test, optimize, and monitor machine learning models in real-world robotic environments.
• Simulation to field: prototype in Isaac Sim/Gazebo; drive flight/ground tests; close the sim-to-real gap with calibration and data feedback loops.
• Sensor fusion & calibration: time sync (PTP), extrinsics/intrinsics, multi-modal fusion, target tracking.
• Quality & safety: bring-up checklists, watchdogs, E-stop and geofencing, regression tests, and performance benchmarking.
• Cross-functional: collaborate with ML, controls, and hardware teams; document designs and handoffs.
What You’ll Bring
• Robotics systems experience shipping real hardware (UAVs, UGVs, or fixed installations).
• Sensors: hands-on with at least two: RGB/thermal cameras, LiDAR, radar, microphone arrays, IMU/GNSS; calibration & synchronization expertise.
• Software: ROS 2, C and/or Python; Linux; git; containerization.
• Machine Learning familiarity: PyTorch or similar; comfortable deploying models at the edge (Jetson); basic CUDA awareness; experience evaluating or integrating ML models into production systems.
• Controls/autonomy basics: planners, controllers, safety states, telemetry.
• VLA exposure: grounding language to action, policy evaluation on robots, prompt/skill graphs, or task planners that translate intents to robot APIs.
• Excellent debugging chops with oscilloscopes/loggers, frame-timing, and field testing under constraints.