AI Engineer - Automotive AI Systems

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.

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.

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.

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)
• CII/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