Senior Machine Learning Engineer

Sensoride has been driven by a simple vision — to make sensing smarter, faster, and more reliable. We create innovative technologies that help industries see and understand the world with unmatched clarity. From improving safety on the road to enabling the next generation of intelligent radar sensors, our solutions turn emerging ideas into reality. Every day, we push the boundaries of what’s possible, because we believe precision matters in mobility. The breakthroughs we’re making today are shaping the smarter, safer, and more accurate sensors — and wherever accuracy matters, you’ll find Sensoride at the center. Why Consider This Job Opportunity The Senior Machine Learning Engineer works on meaningful, real-world challenges where machine learning directly impacts the performance of automotive radar sensing products. This position works alongside a talented team developing cutting-edge technologies. Workplace Policy On-site from San Diego, CA.  After hours support and coverage are required.   What To Expect (Essential Job Responsibilities) Design and develop advanced machine learning models for radar signal processing. Evaluate novel ML and deep learning architecture for radar data. Develop end-to-end ML pipelines, including data preprocessing, feature extraction, model training, validation, and performance optimization for radar data. Analyze and model raw and processed radar data (e.g., time-domain, frequency-domain, range–Doppler, range–angle representations). Drive innovation by evaluating and implementing state-of-the-art ML and deep learning techniques for radar-based detection, classification, and tracking. Optimize models for real-time and embedded deployment, considering constraints such as latency, memory, and power. Bridge theory and practice by translating research outcomes into scalable, real-world applicable algorithms. Support product development through algorithm validation, performance benchmarking, and documentation. Strong statistical and mathematical skills to act as the in-house mathematician/statistician. Miscellaneous Job Responsibilities Review and provide feedback on technical designs, research reports, and algorithm implementations. Represent the team or organization in internal technical forums, design reviews, and external workshops. Ensure adherence to best practices in research methodology, data management, and experimental reproducibility. Assist in defining coding standards, evaluation metrics, and benchmarking methodologies. Promote knowledge sharing through documentation and training sessions. Support hiring activities, including technical interviews and candidate evaluation. What Is Required (Qualifications) Master’s or PhD in Applied Mathematics, Statistics, Electrical Engineering, Computer Science, or a related field. 3+ years’ research experience in developing machine learning models and applied statistics. After hours support and…