Overview

This is the sensor and perception authority at the top of our robotics solutioning funnel.

Responsibilities

• Own sensor and perception feasibility for incoming robotics and Physical AI requirements — given a target outcome, determine which sensors and configurations can meet it, and specify what “good” looks like at the data level (accuracy, coverage, frame, sync).
• Certify device feeds during pilots — validate whether a device delivers the required signal (sub-centimeter tracking, hand keypoints, 3D mesh) against ground-truth expectations, and surface gaps early, before delivery commits.
• Stay device-agnostic across XR headsets, optical/mocap suits, data gloves, depth cameras, and IMU rigs; evaluate and onboard new sensing hardware as it reaches the market and brief the team on what it changes.
• Translate sensor capabilities and limits into recommendations Product, Engineering, and Solutions can act on — and into language clients understand.
• Run and interpret sensor calibration and characterization — intrinsic/extrinsic, multi-sensor alignment, drift/noise, coordinate frames.
• Partner with Robotics Applied AI engineers to turn a validated sensing approach into a working data-collection or perception pipeline.
• Support technical discovery, demos, and proof-of-concepts as the sensing SME alongside solutions leads.
• Maintain clear device profiles, feed specs, and known-limitation notes so the team can reuse your findings rather than re-deriving them.

Qualifications

• 3–5 years hands-on in perception, sensing, or robotics ML — work that touched real sensor data, not simulation or literature alone.
• Sensor calibration and characterization performed personally — intrinsic/extrinsic and multi-sensor alignment — with the ability to explain what drifted, how it was detected, and what it cost.
• Fluent 3D geometry — transformations, camera models, projection, and coordinate frames reasoned about directly, without reaching for a reference.
• Direct experience with at least two of: optical/IR tracking, depth cameras, IMU rigs, optical mocap, XR headset sensor stacks.
• Strong Python, and the judgment to turn a spec sheet or paper into a defensible feasibility answer — grounded in the feed and the physics rather than brand preference, and expressed as accuracy numbers rather than opinion.
• Communicates a sensing trade-off clearly to an engineer and to a client, in the same week, without changing the substance.
• Exposure to egocentric or teleoperation data collection for robot learning.
• Familiarity with ground-truth rigs and accuracy-verification setups.
• LiDAR / point-cloud processing and sensor fusion.

Nice to have

• Perception fundamentals implemented, not only understood — pose estimation, tracking, depth, keypoint estimation.
• Hands-on with XR/egocentric devices (Pico, Quest, or similar) and their tracking / hand-tracking stacks.
• ROS / ROS2 exposure.
• Connections into the research / sensing-hardware ecosystem (e.g., IISc, sensor labs).
• A track record of device profiles, feed specs, or evaluation write-ups that other engineers reused.
• Awareness of the robot-learning and VLA pipelines that consume this sensor data downstream.
• Languages: Python (NumPy, OpenCV, Open3D)
• Perception / 3D: camera models, projection, calibration, point-cloud processing
• Sensing: XR headsets, optical/IR mocap, depth cameras, IMU, data gloves
• Tooling: calibration toolkits; ROS / ROS2 exposure a plus
• Core Areas: sensor feasibility, calibration, tracking/keypoints, 3D geometry, accuracy verification
• Every robotics pilot enters delivery with a sensing approach validated against the client's actual accuracy / quality requirement.
• Device-selection decisions are made on evidence, not assumption — fewer “the tracking is off” surprises after commitment.
• New sensing hardware is evaluated and ready before client demand forces it.
• Product and Engineering treat this role as the trusted first answer to “can this device give us the data we need?”