Qualcomm
Staff Machine Learning Engineer – Automotive Cybersecurity
$162K–$243K
San Diego, California · On-site · Staff · Full-time · Posted Sep 17, 2026
- Experience
- Minimum: 4+ years with a bachelor’s degree, 3+ years with a master’s degree, or 2+ years with a PhD, in Hardware Engineering, Software Engineering, Systems Engineering, or related work. Preferred: 5+ years in machine learning, artificial intelligence, software engineering, or applied intelligent systems; 4+ years with programming languages such as C, C++, Java, or Python.
- Education
- Bachelor’s, master’s, or PhD in Computer Science, Engineering, Information Systems, or a related field.
About the job
Build AI/ML automation for automotive cybersecurity and functional safety engineering, including compliance, security analysis, and product-development workflows.
What you'll do
- Design and deploy AI/ML tools for cybersecurity and functional safety workflows, document review, and engineering productivity.
- Build scalable automation pipelines and knowledge systems using LLMs, GenAI, and agentic AI.
- Apply AI/ML to security analysis, compliance monitoring, process optimization, and future hardware-security and verification workflows.
What they ask for
- Strong background in machine learning and artificial intelligence.
- Experience with LLMs, GenAI, and agentic AI systems or intelligent automation frameworks.
- Strong programming skills in Python, C++, or similar languages, with software engineering fundamentals.
- Bachelor’s degree in a related field and 4+ years of related work experience; or master’s degree and 3+ years; or PhD and 2+ years.
Nice to have
- Familiarity with automotive cybersecurity or functional safety standards, such as ISO/SAE 21434, ISO 26262, or UNECE WP.29.
- Exposure to hardware security, RTL design, design verification, or silicon validation workflows.
Pay
$162,000.00 - $243,000.00 USD base salary
Tools
Python, C++, LLMs, GenAI, RAG, ISO/SAE 21434, ISO 26262, UNECE WP.29
AI focus
Develop and deploy AI/ML automation for engineering productivity, document understanding, compliance, security analysis, functional safety, and design and verification workflows, using LLMs, GenAI, and agentic systems.
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