ROLE SUMMARY

This is the sensor and perception authority at the top of our robotics solutioning funnel. When a client requirement comes in — egocentric data collection, teleoperation, field deployment — you are the person who determines what a given sensing setup can actually produce, whether it meets the requirement (sub-centimeter positional accuracy, hand-keypoint fidelity, a usable 3D mesh), and, when it doesn't, what device or configuration will.

You are deliberately device-agnostic: XR headsets (e.g., Pico), optical/mocap suits (e.g., OptiTrack), data gloves, depth cameras, and IMU-based rigs are all fair game — you reason about the feed, the geometry, and the physics behind it rather than any single product. You work shoulder-to-shoulder with Product, Engineering, and Solutions to certify device feeds for pilots before we commit to a client, and you keep scanning the market so we adopt new sensing hardware ahead of demand, not behind it.

KEY 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.

MUST HAVE

• 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.

PREFERRED QUALIFICATIONS

• 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.

• Experience with optical mocap (OptiTrack, Vicon) or data gloves.

• 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

• 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.

TECHNICAL STACK

• 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

SUCCESS MEASURES

• 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?”

Flex hours: Given global client coverage, this role includes flexibility for periodic night-shift / time-zone-overlap hours to support US/EU engagements as required.