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Robotics Perception information

What are some common challenges faced by professionals in Robotics Perception roles when integrating perception systems with real-world robotics applications?

A key challenge in Robotics Perception is ensuring that perception algorithms—such as object detection, mapping, and localization—operate reliably in dynamic, unstructured environments. Professionals often need to address sensor noise, variable lighting conditions, and occlusions that can impact data quality. Integrating perception systems with robotic hardware requires close collaboration with mechanical, software, and controls engineers to ensure real-time performance and robustness. Adapting solutions to different platforms and continuously validating them in field tests is also an essential, ongoing responsibility.

What is robotics perception?

Robotics perception refers to the ability of robots to interpret and understand their environment using sensors such as cameras, LIDAR, and radar. This field combines elements of computer vision, machine learning, and sensor fusion to help robots identify objects, navigate spaces, and interact with the world safely and effectively. Robotics perception is crucial for autonomous systems like self-driving cars, drones, and industrial robots, enabling them to make decisions based on real-time data from their surroundings.

What is the difference between Robotics Perception vs Robotics Software Engineer?

AspectRobotics PerceptionRobotics Software Engineer
Required CredentialsBachelor's or Master's in Robotics, Computer Science, or related fields; experience with perception algorithmsBachelor's or Master's in Computer Science, Software Engineering, or related; programming skills in C++, Python
Work EnvironmentResearch labs, R&D departments, industry settings focused on sensor data processingDevelopment teams, industrial or research settings, focusing on software development for robots
Industry UsageUsed in autonomous vehicles, drones, and robotic systems for environment understandingDevelops the software that enables robotic functionalities, including perception modules

Robotics Perception specialists focus on developing algorithms that interpret sensor data to understand the environment, while Robotics Software Engineers build the overall software systems that incorporate perception modules. Both roles often collaborate but differ in their core focus and skill sets.

What are the key skills and qualifications needed to thrive as a Robotics Perception Engineer, and why are they important?

To thrive as a Robotics Perception Engineer, you need a solid background in computer vision, machine learning, and robotics, typically supported by a degree in computer science, engineering, or a related field. Experience with tools like ROS (Robot Operating System), OpenCV, and programming languages such as Python or C++ is essential, along with familiarity with sensor technologies and perception algorithms. Strong problem-solving skills, teamwork, and clear communication are crucial soft skills for addressing complex challenges and collaborating effectively. These abilities ensure robust perception systems, enabling robots to interpret and interact safely and efficiently with their environments.
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Robotics Perception jobs near you
Infographic showing various Robotics Perception job openings in California as of June 2026, with employment types broken down into 61% Full Time, 36% Part Time, and 3% Contract. Highlights an 95% Physical, 1% Hybrid, and 4% Remote job distribution.
Staff Perception Engineer - Robotics

Staff Perception Engineer - Robotics

Knightscope

Sunnyvale, CA • On-site

Full-time

Posted yesterday

Job description

Job Summary:
Knightscope is a security technology company building the Nation’s First Autonomous Security Force. They are seeking a Staff Perception Engineer to own the full perception stack for their autonomous security robot platform, focusing on 3D object detection, scene understanding, and real-time threat detection.
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.
Qualifications:
Required:
• 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:
• 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.
Company:
Knightscope specializes in developing autonomous security robots. Founded in 2013, the company is headquartered in Mountain View, USA, with a team of 201-500 employees. The company is currently Growth Stage.

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