> Markdown version of [/jobs/ext/4639487-ai-researcher-perception](https://www.wearedevelopers.com/jobs/ext/4639487-ai-researcher-perception). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Researcher - Perception - **Company:** 1X Technologies, Inc. - **Location:** San Carlos, CA, United States - **Experience:** Expert - **Salary:** $200,000 - $300,000 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Computer Vision, Code Review, Python (Programming Language), Kinematics, Linear Programming, Sensor Fusion, Neural Rendering, Diagnostic Tools, Pytorch, Deep Learning, Gaussian, Pose Estimation - **Published:** November 8, 2026 - **Apply:** https://www.careerbuilder.com/job-details/ai-researcher-perception-san-carlos-ca--9d73af1d-d91f-409c-bed2-c9422646ddd8 ## About the Role Artificial Intelligence (AI), Research Skills, Robotics, Manufacturing, Artificial Intelligence (AI), Prototyping, Geometry, Benchmarking, Vehicle Fleets, Calibration, * Proven track record of shipping perception systems that run on physical hardware in the real world - robotics experience welcome but not required * Demonstrated ability to train, evaluate, and deploy deep learning models for detection, depth, or pose estimation in PyTorch * Deep experience with geometric computer vision, camera-based kinematic calibration (hand-eye / foot-eye calibration), and camera calibration including intrinsics & extrinsics * Experience owning a capability end to end, from problem definition through deployment and iteration on real failures * Strong Python and modern C++ in large shared codebases, with solid testing and code review practices * 5+ years of relevant industry experience, or an advanced degree plus equivalent hands-on experience Preferred Skills * Experience with visual-inertial odometry or SLAM, including multi-sensor time synchronization and fusion * Familiarity with robot kinematics and frame transforms, and how calibration error shows up in manipulation and locomotion * Experience building and adapting segmentation and classification models, including fine-tuning open-vocabulary or foundation backbones for a specific domain * Experience reconstructing scenes and terrain from video using differentiable rendering, neural rendering, or Gaussian splatting * Exposure to automating calibration and validation in manufacturing, or to building field diagnostic tools for deployed hardware * Familiarity with sensing beyond RGB such as radar, thermal, or near-infrared ## Description * Kinematic Calibration: Recover each robots true geometry from camera data so controls and learned policies act on an accurate model of the body they drive. * 3D Scene Understanding: Deliver depth and scene reconstruction accurate enough to drive terrain-aware locomotion and contact-rich manipulation. * Pose Estimation: Ship human & object pose estimation models that can be used for large scale data labelling efforts and human/robot motion generation models. * Sensor Fusion: Drive time synchronization and fusion across cameras and inertial sensors so every downstream consumer gets spatially and temporally consistent data. * Calibration at Scale: Own intrinsic and extrinsic camera calibration - automated on the production line, monitored for drift across the fleet, and recoverable through field tools. * Evaluation: Establish the datasets and metrics that show whether a perception change improved robot behavior, not just an offline score. Key Competencies * Geometric Vision: Fluent in camera models, multi-view geometry, and non-linear optimization; reaches for classical or learned methods based on the problem. * Production ML: Trains and ships modern vision models, including transformer backbones, generative approaches, and learned depth, and makes them fast and predictable on embedded compute. * Hardware Grounding: Treats sensor noise, timing, and calibration drift as first-class problems, and traces how each propagates into a control loop or policy rollout. * Prototype to Production: Stands up a new capability from a paper and a test rig, then makes it repeatable across a fleet and monitored in the field. * Technical Direction: Sets the approach for a problem area, defends tradeoffs across sensing, models, and compute budget, and stays hands-on.