Robotics Perception/Sensor AI Engineer
$3,500,000–$4,500,000 year
On-siteBengaluru, Karnataka, India
Job Summary
Own sensor and perception feasibility for incoming robotics and Physical AI requirements by determining which sensing setups meet client targets like sub-centimeter accuracy and hand-keypoint fidelity. Certify device feeds during pilots by validating signal quality against ground-truth and surfacing gaps before delivery commits. Stay device-agnostic across XR headsets, optical/mocap suits, depth cameras, and IMU rigs to evaluate new hardware and brief the team on market changes. Translate sensor capabilities into actionable recommendations for Product and Engineering while running calibration and characterization to manage drift and noise. Partner with Applied AI engineers to build data-collection pipelines and support technical discovery as the sensing SME. Maintain clear device profiles and feed specs so the team reuses findings rather than re-deriving them.
Required 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
- Flexibility for periodic night-shift / time-zone-overlap hours to support US/EU engagements as required
Desired 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
- 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
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