Senior Machine Learning Engineer

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 coverage are required.
• Strong understanding of radar fundamentals and signal processing concepts.
• Proven experience applying ML/DL techniques (e.g., CNNs, RNNs, Transformers, classical ML) to radar or similar sensing modalities.
• Proficiency in Python and ML frameworks such as PyTorch or TensorFlow; experience with data analysis tools (NumPy, SciPy, pandas).
• Ability to train, build, deploy, and manage machine learning models for radar applications such as Google Colab, AWS, or similar platforms.
• Problem-solving skills with the ability to work across multidisciplinary teams.
• Strong communication skills with the ability to explain complex technical concepts clearly.

How To Stand Out (Preferred Qualifications)

• Experience applying machine learning to radar, perception, robotics, or autonomous systems.
• Deep understanding of signal processing and sensor data analysis.
• Proven track record of designing ML models that deliver measurable product impact.
• Experience with time domain samples, data augmentation and field testing.
• Publications, patents, or noteworthy technical contributions in ML or sensing technologies.
• Curiosity, ownership, and a passion for building cutting-edge products with a high-performing team.

Other

Minimum Salary: $140,000

Maximum Salary: $178,000

We consider various factors in determining actual pay including your skills, qualifications, and experience. In addition to salary, this position is eligible for incentive awards based on individual and business performance as well as competitive benefits.

Perks

• Comprehensive benefits package including medical, dental, and vision insurance.
• Generous Paid Time Off including paid holidays and floating holidays.
• 401(k) employer match on retirement planning.
• Hybrid working schedule for eligible positions.
• Tuition reimbursement on approved programs.
• Flexible and health spending accounts.
• Talent Development program.