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Bengaluru, Karnataka, India
Hybrid
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.
