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About This Role
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
Salary Context
This $140K-$175K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Knightscope, Inc., this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($157K) sits 13% below the category median. Disclosed range: $140K to $175K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Knightscope, Inc. AI Hiring
Knightscope, Inc. has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Sunnyvale, CA, US. Compensation range: $175K - $275K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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