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Telus Digitalvia Ashby
Robotics Perception/Sensor AI Engineer
Bengaluru$3500K - $4500K/yrPosted 5d ago
Computer VisionMid LevelFull-time
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About the Role
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.
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.
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