This is a job posting for a Radar AI Engineer at Husense, a company building radar + ML solutions for smart cities, sports analytics, smart buildings, and defense. They have real customers like the Dutch police and KLM, and are looking for someone to own the full pipeline from raw radar signals to deployed embedded models. The role requires significant expertise in ML, signal processing, and embedded systems.
What they want, where you stand, and the exact résumé edits to qualify.
Biggest lever: Lack of core academic background and practical experience in signal processing, deep learning model training (PyTorch), and embedded deployment for real-time sensor data.
A starter prompt for Claude Code, what you'll need, and how to reach them.
This is a job application for a 'Radar AI Engineer' position at Husense. The prompt for Claude Code would be to assist in crafting a compelling application, not to build a product. Focus on: 1. Analyzing the job description to extract key requirements (MSc/PhD in ML/EE/signal processing, PyTorch, Python, embedded deployment, Rust/C++). 2. Generating bullet points for a cover letter highlighting experience in ML pipeline ownership, signal processing, and embedded model deployment (e.g., ONNX, TensorRT, edge TPUs). 3. Brainstorming potential projects from my portfolio that best align with 'raw FMCW/MIMO signal in, deployed embedded model out' pipeline, even if not radar-specific (e.g., audio signal processing + embedded ML, sensor fusion). 4. Drafting an email to 'radar-ai-engineer@mail.husense.io' with a concise cover letter and a brief summary of one highly relevant project. Ensure the tone is professional and confident, emphasizing the full-stack ownership aspect. Provide specific examples of how past work demonstrates the required skills.
Husense | https://husense.io | Radar AI Engineer | The Hague, NL | ONSITE (80%, 1 flexible day/week) | Full-time We build radar + ML for smart cities, sports analytics, smart buildings, and defense perception. Our sensors see what cameras can't — 24/7, through walls/weather/crowds, with no faces and no audio. Customers include the Dutch police, Gemeente Den Haag, Gemeente Amsterdam, KLM, Koninklijke Marechaussee. TU Delft partner. Hiring our first Radar AI Engineer to own the full pipeline end-to-end: raw FMCW/MIMO signal in, deployed embedded model out (ONNX, TensorRT, edge TPUs). Real hardware. Real customers. Privacy-first by design. Looking for: MSc/PhD in ML / EE / signal processing; strong PyTorch + Python; embedded deployment experience; Rust or C/C++ a plus. EU-based or NL-visa-eligible (we sponsor highly-skilled-migrant + 30%-ruling for the right person). Process: first call in 5 days, decision in 3 weeks. Founder-led, no multi-month limbo. Apply: https://husense.odoo.com/jobs/radar-ai-engineer-6 or email radar-ai-engineer@mail.husense.io with the one project you're proudest of.
This role is a significant reach due to the specialized domain knowledge (radar signal processing, embedded ML, PyTorch) and the onsite NL requirement. A concrete study plan would involve a Master's degree in ML/EE/Signal Processing (~2 years), followed by building several personal projects involving PyTorch model training for sensor data and deploying them to edge devices (e.g., Raspberry Pi with custom models) over 6-12 months. This is a complete career pivot.
Requires deep foundational knowledge not acquired by a solo dev in weeks. This is a prerequisite for the role itself.
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Standard ML stack for many developers.
New domain for pure web/app developers; requires understanding of hardware constraints and model optimization for edge devices. ~1-2 months to gain practical experience.
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Common for performance-critical embedded systems; learning curve if not already proficient. ~1 month for basic proficiency in one.
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Geographical and legal requirement for the job. Not a 'skill' to learn.
Apply via their Odoo job portal (https://husense.odoo.com/jobs/radar-ai-engineer-6) or email 'radar-ai-engineer@mail.husense.io' with your proudest project.
“To the hiring manager at Husense: 'I am writing to express my strong interest in the Radar AI Engineer position. My background aligns well with your need for end-to-end ML pipeline ownership, from raw sensor data to deployed embedded models. I'm particularly proud of [describe one project that showcases signal processing, ML, and embedded deployment], which I believe demonstrates my capability to contribute to your privacy-first radar solutions. Please find my CV attached.'”
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