Staff Machine Learning Engineer – Automotive Cybersecurity

Company: Qualcomm Technologies, Inc. Job Area: Engineering Group, Engineering Group > Machine Learning Engineering General Summary: We are seeking a Staff Machine Learning Engineer to drive AI/ML-based automation across Automotive SoC Cybersecurity and Safety engineering at Qualcomm. This role focuses on building intelligent, scalable solutions that improve engineering productivity, streamline cybersecurity and functional safety workflows, and enable automation across the full product development lifecycle. The ideal candidate will contribute across a broad range of problem spaces — from AI-assisted engineering tools and automated compliance workflows, to future-looking capabilities in hardware security, functional safety, silicon verification, and AI-driven design automation. This role sits at the intersection of AI/ML, cybersecurity, functional safety, semiconductor engineering, and intelligent systems, contributing to a long-term automation vision aligned with automotive cybersecurity standards such as ISO/SAE 21434. Technical Skills and Expertise 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 solid software engineering fundamentals Experience building scalable, production-quality ML pipelines, model training workflows, or inference systems Experience with knowledge systems, semantic search, retrieval-augmented generation (RAG), or workflow orchestration platforms Familiarity with automotive cybersecurity or functional safety standards (e.g., ISO/SAE 21434, ISO 26262, UNECE WP.29) is a plus Exposure to hardware security concepts, RTL design, design verification, or silicon validation workflows Role and Responsibilities Design and develop AI/ML-based automation solutions for cybersecurity and functional safety engineering workflows Build intelligent systems for document understanding, review automation, and engineering productivity Develop and deploy solutions leveraging LLMs, GenAI, and agentic AI frameworks Contribute to knowledge automation platforms that support cross-project reuse and decision support Build scalable, end-to-end automation pipelines spanning multiple engineering domains Apply AI/ML to security analysis, compliance monitoring, and process optimization Support development of future capabilities in hardware security analysis, verification automation, and AI-assisted design workflows Collaborate with cross-functional teams across cybersecurity, functional safety, systems, software, design, and verification Minimum Qualifications: • Bachelor's degree in Computer Science, Engineering, Information Systems, or related field and 4+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience. OR Master's degree in Computer Science, Engineering, Information Systems, or related field and 3+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience. OR PhD in Computer Science, Engineering, Information Systems, or related field and 2+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.

Master's or PhD degree in Computer Science, Electrical Engineering, Artificial Intelligence, Machine Learning, or a related technical field 5+ years of experience in machine learning, artificial intelligence, software engineering, or applied intelligent systems 4+ years of work experience with Programming Language such as C, C++, Java, Python, etc. Experience designing, developing, and deploying ML or AI-based solutions in production or engineering environments Experience with agentic AI systems, multi-agent orchestration frameworks, or autonomous workflow automation Familiarity with automotive cybersecurity or functional safety standards such as ISO/SAE 21434, ISO 26262, or