Staff Perception Engineer – Robotics

Knightscope•Sunnyvale, CA

8d•$240,000 - $275,000•Onsite

About The Position

In this role, you will own the full perception stack for our autonomous security robot platform, from raw LiDAR, camera, and thermal sensor data through 3D object detection, scene understanding, and persistent entity tracking. You will be a technical lead working across robotics, ML, and systems engineering to ship production-grade perception capabilities that power real-time threat detection, incident correlation, and evidence capture in the field.

Requirements

7+ years of hands-on experience at an OEM, AV company, physical security platform, or robotics tech company shipping perception software to production.
Deep expertise in 3D computer vision: LiDAR/camera 3D object detection, point cloud processing, multi-view geometry, and sensor fusion.
Strong ML fundamentals, CNNs, Transformers, multi-task architectures, with production deployment experience (TensorRT, ONNX, CUDA optimization).
Fluency in C++ (real-time systems) and Python; experience with ROS2 and simulation environments (CARLA, Isaac).
Production-first mindset demonstrated ability to take models from research to deployed, real-world systems operating under real operational constraints.

Nice To Haves

Exposure to vision-language models (VLMs): fine-tuning, prompting, or integrating VLM outputs into a perception pipeline for scene understanding or anomaly narration.
Familiarity with Vision-Language-Action (VLA) or end-to-end policy models that map sensor observations directly to robot actions, and interest in applying these to active confirmation and repositioning behaviors.
Experience with ALPR, face recognition, or ReID systems in deployed security or automotive contexts.
Familiarity with evidence integrity, chain-of-custody media, or tamper-evident capture pipelines.
Publications or open-source contributions in 3D CV, embodied AI, or autonomous systems.
MS or PhD in Computer Science, Robotics, Electrical Engineering, or related field.

Responsibilities

Design, train, and deploy 3D perception models, object detection, segmentation, and multi-object tracking, from multi-modal sensor data (LiDAR, camera, thermal, radar) using state-of-the-art BEV and transformer-based architectures.
Own the end-to-end ML pipeline from large-scale data curation and annotation strategy through model training, optimization (TensorRT/CUDA), and real-time onboard deployment on edge compute.
Build persistent cross-sensor identity: the same person, vehicle, or license plate maintains a stable track across camera handoffs, patrol legs, and time, feeding entity correlation and incident generation downstream.
Design and implement evidence-first capture: pre/post-event clips, full-frame and crop media, and structured detection output that is replay-safe and analyst-ready.
Explore and integrate Vision-Language Models (VLMs) to enrich detection outputs with scene narration, anomaly reasoning, and long-tail incident understanding, moving K7 beyond bounding boxes toward analyst-ready scene descriptions.
Drive technical decisions on architecture, data strategy, and roadmap; mentor engineers across the team.
Design and run rigorous offline and closed-loop evaluations; define metrics for safety-critical perception performance across diverse real-world deployment environments.