AI Validation Engineer

<p><span>As AI-powered features become central to automotive vehicles — from ADAS perception and voice assistants to predictive diagnostics and intelligent infotainment — rigorous validation of these systems is critical for successful deployment. We are seeking a Senior AI Validation Engineer to design and implement testing strategies, frameworks, and automated pipelines that ensure the quality, safety, and reliability of deep learning and LLM-based features across vehicle platforms.<br/><br/></span></p> <p><span>This role sits at the intersection of AI/ML engineering and automotive system validation. You will build the tools, datasets, and evaluation methodologies that enable confident delivery of AI-driven automotive solutions.</span></p> <p> </p> <p><span><strong>Key Responsibilities:</strong></span></p> <ul> <li><span>Design and implement validation frameworks for deep learning models (perception, NLP, generative AI) deployed in automotive systems, covering accuracy, robustness, latency, and safety metrics.</span></li> <li><span>Develop automated test pipelines for LLM-based features, including hallucination detection, response quality evaluation, prompt regression testing, and adversarial input testing.</span></li> <li><span>Build and curate evaluation datasets and benchmarks tailored to automotive AI use cases (e.g., voice commands, diagnostic Q&amp;A, sensor fusion outputs).</span></li> <li><span>Create AI-assisted test generation tools that leverage LLMs to automatically produce test cases, test data, and expected-result specifications from system requirements.</span></li> <li><span>Develop model monitoring and drift detection systems for AI features running in production and test environments.</span></li> <li><span>Collaborate with system architects to integrate AI model validation into existing test bench infrastructure and CI/CD pipelines.</span></li> <li><span>Implement automated regression testing for ML model updates, ensuring backward compatibility and performance parity across software releases.</span></li> <li><span>Analyze test results using statistical methods and ML techniques to identify root causes, failure patterns, and quality trends.</span></li> <li><span>Work in cross-functional Agile teams spanning AI/ML, embedded software, and system integration disciplines.</span></li> </ul> <p><span> </span></p> <strong><span>Qualifications</span></strong> <p><span><strong>Basic Qualifications:</strong></span></p> <ul> <li><span>Bachelor’s degree in computer science, Machine Learning, Data Science, Electrical Engineering, or a related field.</span></li> <li><span>Minimum of 5 years of experience in ML/AI development, with a minimum of 2 years focused on model evaluation, testing, or validation.</span></li> <li><span>Strong proficiency in Python and testing/automation frameworks (pytest, Robot Framework, or equivalent).</span></li> <li><span>Hands-on experience evaluating deep learning models — including metrics design, dataset curation, bias/fairness analysis, and regression testing.</span></li> <li><span>Experience with LLM evaluation techniques (BLEU, ROUGE, human-in-the-loop evaluation, LLM-as-judge approaches).</span></li> <li><span>Familiarity with ML experiment tracking and pipeline orchestration tools (MLflow, Weights &amp; Biases, Kubeflow, or equivalent).</span></li> <li><span>Experience with CI/CD systems (Jenkins, GitLab CI, GitHub Actions) for automated test execution.</span></li> <li><span>Strong analytical and communication skills with the ability to translate AI validation results into actionable insights for engineering teams.</span></li> </ul> <p><span><strong>Preferred Qualifications:</strong></span></p> <ul> <li><span>Master's in Computer Science, Machine Learning, or a related field.</span></li> <li><span>Experience with simulation-based testing or digital twin environments.</span></li> <li><span>Familiarity with automotive test toolchains (dSpace, Vector CANoe, NI VeriStand) is a plus but not required.</span></li> <li><span>Ability to collaborate effectively across time zones with global engineering teams.</span></li> <li><span>Knowledge of automotive safety standards (ISO 26262, SOTIF/ISO 21448) as applied to AI systems.</span></li> <li><span>Experience with adversarial robustness testing, out-of-distribution detection, or uncertainty quantification for neural networks.</span></li> </ul>