Staff Perception Engineer – Robotics

Knightscope, Inc.; Sunnyvale (CA); On-site; USD 240,000 - 275,000; Full time

Benefits offered by this job

Stock options

Medical insurance

Dental & vision

401(k)

Paid time off

Job summary

Knightscope, Inc. in Sunnyvale, CA, seeks an experienced engineer to own the perception stack for our autonomous security robot platform, from LiDAR and camera data to 3D object detection and persistent tracking.

You will lead ML pipelines, optimize for edge compute, mentor teammates, and help shape the roadmap while ensuring production-grade reliability in real-world security deployments.

Qualifications

• 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.

Responsibilities

• Own the full perception stack for autonomous security robot platform, from raw sensor data to 3D understanding and persistent tracking.
• Lead end-to-end ML pipeline from data curation and annotation strategy through model training and real-time onboard deployment on edge compute.
• Build persistent cross-sensor identities across camera handoffs, patrol legs, and time.
• Design and implement evidence-first capture with replay-safe media and structured outputs for analysts.
• Mentor engineers, drive architecture decisions, and define safety-critical evaluation metrics.

Skills

3D perception

LiDAR & camera fusion

TensorRT

CUDA optimization

C++

Python

ROS2

CARLA/Isaac

Education

MS/PhD in CS/Robotics

Tools

CARLA

Isaac

ONNX

CUDA

Job description

AboutKnightscope

Knightscope (NASDAQ: KSCP) is a security technology company building the nation's first Autonomous Security Force — autonomous machines, AI-driven software, and elite security professionals operating as one integrated managed service. The Company is on a mission to make the United States of America the safest country in the world. One Team. One Force.

About The Role

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.

Location Requirement: Full-time, on-site at Sunnyvale HQ (No relocation provided)

Key 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.

Required Qualifications

• 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.

Preferred Qualifications

• 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.

Compensation & Benefits

• Base Salary: $240,000 to $275,000 (DOE)
• Equity: Stock options
• Benefits: Medical, dental, vision, 401(k), paid time off
• Location Requirement: Full-time, on-site at Sunnyvale HQ