Senior AI/ML Engineer

About Knightscope

Knightscope is a security technology company building the Nation’s First Autonomous Security Force. The Company combines autonomous machines, advanced software, and human expertise to help protect people, property, and critical infrastructure. Knightscope’s long-term mission is to make the United States of America the safest country in the world.

About the Role

Knightscope is seeking two Senior AI/ML Engineers to own the machine learning detection pipelines running on the Intelligent Control Module across our new K1, H1, and K7 autonomous security robots. The ICM runs a full edge inference stack on NVIDIA Jetson hardware: a Deep Stream-based multi-model detection pipeline covering people, vehicle, license plate, and face detection — all executing concurrently at real-time frame rates on constrained onboard hardware. In addition to owning the onboard detection pipeline, these engineers will also architect the AI intelligence layer for the Signals platform: a prioritization engine, pattern detection system, recommendation scorer, explain ability module, and continuous feedback loop that transforms raw robot detections into actionable security intelligence. This is a hands-on production engineering role — you will own model training, optimization, deployment, and ML Ops lifecycle end-to-end.

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

Key Responsibilities

• Own and maintain the onboard detection pipeline running on the ICM across the new K1, H1, and K7 robots: Deep Stream multi-model architecture, YOLOv9/YOLO-family detection models for people, vehicle, license plate, and face detection, GPU-accelerated inference on NVIDIA Jetson Orin NX and Xavier.


• Optimize edge inference performance: model quantization (INT8/FP16), Tensor RT engine compilation, DLA offloading, and latency profiling to meet real-time frame rate targets under concurrent multi-model load.


• Optimize edge inference performance: model quantization (INT8/FP16), Tensor RT engine compilation, DLA offloading, and latency profiling to meet real-time frame rate targets under concurrent multi-model load.


• Architect and build the Signals AI intelligence layer: prioritization engine, pattern detection, recommendation scorer, explain ability module, and human-in-the-loop feedback pipeline.


• Integrate foundation model APIs (Open AI, Anthropic, or equivalent) into the Signals intelligence stack for context enrichment, anomaly summarization, and operator-facing recommendations.


• Build and maintain ML Ops infrastructure: model versioning with ML flow or equivalent, automated training pipelines, CI/CD for model deployment, and production monitoring for accuracy drift and inference latency.


• Define and maintain model evaluation frameworks, benchmark datasets, and performance regression tests to ensure detection quality across firmware and hardware updates.


• Collaborate with the ICM Principal Architect, Full Stack engineers, and the Senior Audio/Video team to integrate ML outputs cleanly into the broader ICM and Signals platform.


• Mentor junior engineers; contribute to architecture reviews and technical documentation for the ML stack.

Required Qualifications

• 5–10 years of software engineering experience with a focus on applied machine learning and computer vision in production environments — not research.


• Deep hands-on expertise with NVIDIA Deep Stream SDK: multi-model pipeline design, Gst-nvinfer plugin configuration, primary and secondary inference graphs, and custom output layer parsers.


• Strong proficiency with YOLO-family models (YOLOv8, YOLOv9, YOLO11): training, fine-tuning on custom datasets, ONNX export, and Tensor RT engine optimization.


• Hands-on experience with NVIDIA Jetson platforms (Orin NX, Xavier, or equivalent): Tensor RT INT8/FP16 quantization, DLA offloading, GPU memory management, and latency benchmarking.


• Experience with multi-modal sensor fusion and multi-camera detection pipelines is a strong differentiator.


• Proficiency in Python for ML engineering; C++ for performance-critical inference code and Deep Stream custom plugins.


• Experience building ML Ops pipelines: ML flow or equivalent for experiment tracking and model versioning, automated training with Kubeflow or similar, and production drift monitoring.


• Familiarity with foundation model APIs (Open AI, Anthropic, or equivalent) and RAG/agentic architectures for intelligence enrichment use cases.


• BS/MS in Computer Science, Electrical Engineering, or related field — or equivalent professional experience.

Compensation & Benefits

• Base Salary: $140,000 – $175,000 each (DOE)


• Equity: Stock options


• Benefits: Medical, dental, vision, 401(k), paid time off


• Location Requirement: Full-time, on-site at Sunnyvale HQ