AI Engineer - Automotive AI Systems

AI Engineer — Automotive AI SystemsAI is no longer a feature in modern vehicles — it is the vehicle. ADAS perception, voice assistants, predictive diagnostics, and intelligent infotainment are now central to how drivers experience and trust their cars. Getting these systems wrong isn't a software bug — it's a safety event.We are looking for an AI Engineer who builds the frameworks, pipelines, and methodologies that stand between an AI model and a vehicle on the road. You will be the quality and safety gate for deep learning and LLM-based features across our vehicle platforms — designing the tests, the tools, and the benchmarks that give engineering teams confidence to ship.This is a high-impact role at the intersection of AI/ML engineering and automotive system validation. Your work directly determines whether AI-driven features are safe, reliable, and ready for production.What You Will Own:AI Frameworks:Design and implement end-to-end AI frameworks for deep learning models — perception, NLP, generative AI — covering accuracy, robustness, latency, and functional safety metrics across automotive deployment environments.LLM development and validation Pipelines:Build automated evaluation pipelines for LLM-based features including hallucination detection, response quality scoring, prompt regression testing, and adversarial input coverage. Ensure every model update is tested before it reaches a vehicle.Automotive AI Benchmarks:Build and curate evaluation datasets and benchmarks purpose-built for automotive AI use cases — voice command recognition, diagnostic Q&A, sensor fusion output validation, and edge-case scenario coverage.AI-Assisted Test Generation:Leverage LLMs to automatically generate test cases, test data, and expected-result specifications directly from system requirements — reducing manual test authoring and increasing coverage systematically.Production Monitoring & Drift Detection:Develop model monitoring systems that detect performance degradation, distribution shift, and drift in AI features operating in both test environments and production vehicles.CI/CD Integration:Embed AI model validation into existing test bench infrastructure and CI/CD pipelines — making automated regression testing a standard gate for every ML model update and software release.Root Cause & Quality Analysis:Apply statistical methods and ML techniques to test results to identify failure patterns, root causes, and quality trends — and translate findings into clear, actionable recommendations for engineering teams.Basic Qualifications:Bachelor's degree in Computer Science, Machine Learning, Data Science, Electrical Engineering, or related fieldA minimum of 3 years in ML/AI development; with at least a minimum of 1 year focused on model evaluation, testing, or validationStrong Python proficiency and hands-on experience with testing frameworks (pytest, Robot Framework, or equivalent)Deep experience evaluating deep learning models — metrics design, dataset curation, bias analysis, regression testingPractical knowledge of LLM evaluation techniques: BLEU, ROUGE, LLM-as-judge, human-in-the-loop approachesExperience with ML experiment tracking and pipeline orchestration (MLflow, Weights & Biases, Kubeflow, or equivalent)CI/CD experience (Jenkins, GitLab CI, GitHub Actions) for automated test execution at scaleAbility to communicate complex AI validation results clearly to cross-functional engineering and leadership audiencesPreferred Qualifications:Experience with simulation-based testing or digital twin environmentsKnowledge of automotive safety standards — ISO 26262, SOTIF/ISO 21448 — applied to AI systemsAdversarial robustness testing, out-of-distribution detection, or uncertainty quantification for neural networksFamiliarity with automotive test toolchains (dSpace, Vector CANoe, NI VeriStand)Proven ability to collaborate across time zones with global, cross-disciplinary engineering teamsBrand: FCA GroupJob ID: 2019696Employment Type: Full-timeJob Area: SoftwarePosted Date: 08/07/2026