AI Validation Engineer

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.

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.

Key Responsibilities

• * Design and implement validation frameworks for deep learning models
• (perception, NLP, generative AI) deployed in automotive systems, covering
• accuracy, robustness, latency, and safety metrics.
• * Develop automated test pipelines for LLM-based features, including
• hallucination detection, response quality evaluation, prompt regression
• testing, and adversarial input testing.
• * Build and curate evaluation datasets and benchmarks tailored to automotive AI
• use cases (e.g., voice commands, diagnostic Q&A, sensor fusion outputs).
• * Create AI-assisted test generation tools that leverage LLMs to automatically
• produce test cases, test data, and expected-result specifications from system
• requirements.
• * Develop model monitoring and drift detection systems for AI features running
• in production and test environments.
• * Collaborate with system architects to integrate AI model validation into
• existing test bench infrastructure and CI/CD pipelines.
• * Implement automated regression testing for ML model updates, ensuring
• backward compatibility and performance parity across software releases.
• * Analyze test results using statistical methods and ML techniques to identify
• root causes, failure patterns, and quality trends.
• * Work in cross-functional Agile teams spanning AI/ML, embedded software, and
• system integration disciplines.

Qualifications

• Basic

Qualifications

• * Bachelor’s degree in computer science, Machine Learning, Data Science,
• Electrical Engineering, or a related field.
• * Minimum of 5 years of experience in ML/AI development, with a minimum of 2
• years focused on model evaluation, testing, or validation.
• * Strong proficiency in Python and testing/automation frameworks (pytest, Robot
• Framework, or equivalent).
• * Hands-on experience evaluating deep learning models — including metrics
• design, dataset curation, bias/fairness analysis, and regression testing.
• * Experience with LLM evaluation techniques (BLEU, ROUGE, human-in-the-loop
• evaluation, LLM-as-judge approaches).
• * Familiarity with ML experiment tracking and pipeline orchestration tools
• (MLflow, Weights & Biases, Kubeflow, or equivalent).
• * Experience with CI/CD systems (Jenkins, GitLab CI, GitHub Actions) for
• automated test execution.
• * Strong analytical and communication skills with the ability to translate AI
• validation results into actionable insights for engineering teams.

Preferred Qualifications

• * Master's in Computer Science, Machine Learning, or a related field.
• * Experience with simulation-based testing or digital twin environments.
• * Familiarity with automotive test toolchains (dSpace, Vector CANoe, NI
• VeriStand) is a plus but not required.
• * Ability to collaborate effectively across time zones with global engineering
• teams.
• * Knowledge of automotive safety standards (ISO 26262, SOTIF/ISO 21448) as
• applied to AI systems.
• * Experience with adversarial robustness testing, out-of-distribution
• detection, or uncertainty quantification for neural networks.