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.

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

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.

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.