# AI Engineer — Automotive AI Systems AI 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 field - A minimum of 3 years in ML/AI development; with at least a minimum of 1 year focused on model evaluation, testing, or validation - Strong 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 testing - Practical knowledge of LLM evaluation techniques: BLEU, ROUGE, LLM-as-judge, human-in-the-loop approaches - Experience 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 scale - Ability to communicate complex AI validation results clearly to cross-functional engineering and leadership audiences **Preferred Qualifications:** - Experience with simulation-based testing or digital twin environments - Knowledge of automotive safety standards — ISO 26262, SOTIF/ISO 21448 — applied to AI systems - Adversarial robustness testing, out-of-distribution detection, or uncertainty quantification for neural networks - Familiarity with automotive test toolchains (dSpace, Vector CANoe, NI VeriStand) - Proven ability to collaborate across time zones with global, cross-disciplinary engineering teams