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Open nowPosted 137 days ago

Senior Perception Engineer

xDOF23 open roles

Where
San Mateo Hybrid
Work mode
Hybrid
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Your applicationOpen nowSenior Perception EngineerxDOF · San Mateo Hybrid

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The clock on this job

Early applications get read.

13.1% of postings close within 7 days. Measured by our own scanner across the market. xDOF postings stay open a median of 36 days.

Share of postings closed within
  1. 2.2%1 day
  2. 4.7%3 days
  3. 13.1%7 days
  4. 21.1%14 days
  5. 35.7%30 days
  6. 58.7%60 days
This job: posted 137 days ago

xDOF median: 36 days open

The posting

At XDOF, we’re at an inflection point. Frontier labs are racing to build general-purpose robots, and high-quality training data is the bottleneck. We’re building the foundation behind the foundation models – the data collection systems, operational capability, exabyte-scale data warehouse, and software toolchain – to help our partners drive the field forward.

The Perception Algorithm team transforms raw multimodal sensor data into high-quality robot training annotations. You will be deeply involved in the complete loop from data collection to model delivery — sensor calibration, SLAM localization, human pose estimation, perception model training, and embedded deployment. Your work directly determines the quality ceiling of our training data.

Core Responsibilities

Human Pose Estimation

- Design and optimize hand pose estimation pipelines supporting accurate joint angle extraction from teleoperation data collection

- Build full-body pose estimation systems for motion capture and teleoperation action annotation ground truth generation

- Research and apply vision-based pose estimation methods (markerless) to reduce data collection costs

- Retargeting pose estimation outputs with robot joint angle data to generate consistent training annotations

Robot Perception & Calibration

- Design and maintain intrinsic/extrinsic calibration pipelines for multi-camera arrays (factory calibration + online recalibration)

- Build visual SLAM / V-SLAM systems supporting real-time localization and scene reconstruction on data collection platforms

- Implement hand-eye calibration between cameras and robot end-effectors

- Develop temporal alignment solutions across multimodal sensors (cameras, IMU, data gloves, force sensors)

Perception Model Training & Deployment

- Train and iterate on perception models including object detection, instance segmentation, and 6DoF pose estimation

- Optimize model inference using TensorRT / CUDA for real-time performance on robot embedded platforms

- Write custom CUDA kernels for low-level acceleration of perception tasks

- Design evaluation metric frameworks for perception models; continuously track the relationship between model performance and data quality

End-to-End Loop from Data Collection to Model Delivery

- Contribute to the design of automated annotation pipelines that convert sensor data into structured training labels

- Build Auto QA modules to filter low-quality data including anomalous frames, failed demonstrations, and sensor dropouts

- Collaborate with ML engineers and data infrastructure teams to ensure perception output formats meet downstream VLA model training requirements

- Establish feedback mechanisms linking perception accuracy to model training outcomes, continuously improving annotation quality

Requirements

Must-Have

- 5+ years of industry experience in robot perception or computer vision

- Strong 3D vision fundamentals: stereo and structured-light camera principles, 3D reconstruction

- Proficiency with SLAM frameworks (ORB-SLAM, VINS-Mono, FastLIO, etc.) or V-SLAM system development experience

- Hands-on engineering experience with human pose estimation: hand joints (MediaPipe, MANO) or full-body pose (OpenPose, SMPLify, etc.)

- Proficient in deep learning training frameworks for perception model training, tuning, and evaluation

- TensorRT deployment experience with real-time inference optimization on embedded platforms (Jetson, Horizon, etc.)

- CUDA programming fundamentals; ability to write or debug custom kernels

- Proficient in C++ and Python with ROS / ROS2 development experience

- Proficient with AI coding agents

Nice to Have

- Engineering experience with 6DoF object pose estimation (FoundPose, FoundationPose, GDR-Net, etc.)

- Familiarity with 3D Gaussian Splatting or NeRF for scene reconstruction or data augmentation

- Experience with robot manipulation or teleoperation systems

- End-to-end development experience with automated annotation pipelines or ground truth generation systems

- Published research in perception, pose estimation, or robotics

What We Offer

- Direct involvement in the most critical technical challenge in embodied intelligence: producing high-quality robot training data

- An environment working alongside top-tier robotics engineers and ML researchers

- Proprietary hardware platforms (humanoid robots, camera arrays, data gloves)

- A fast-paced, high-autonomy 0→1 work environment

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