Applied Machine Learning Engineer – Vision Language Models

A company is developing conversational driving experiences that combine state-of-the-art Vision Language Models with advanced vehicle capabilities. The goal is to create AI systems that understand driver intent and connect natural language instructions with parking and driving functions. The project involves close collaboration with ML researchers and ADAS engineers to transform emerging AI technologies into practical automotive experiences.

The role combines hands-on machine learning development, experimentation, evaluation, and data engineering. It is ideal for engineers who enjoy building and improving AI systems while working on real-world applications.

Key responsibilities:

• Develop and evaluate VLM-based solutions for conversational parking and related driving use cases.
• Build datasets, benchmarks, and evaluation frameworks for model assessment.
• Design and execute experiments to improve model accuracy, robustness, and reliability.
• Support model adaptation through prompting, fine-tuning, and data-centric approaches.
• Analyze model failures and identify opportunities for improvement.
• Develop tooling for data preparation, testing, and automated evaluation.
• Collaborate with integration engineers to support deployment and validation activities.
• Monitor developments in multimodal AI, vision-language models, and embodied AI systems.
• Document findings and communicate technical results to project stakeholders.
• Contribute to the development of future AI-enabled automotive experiences.

Must Have Requirements:

• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Robotics, Electrical Engineering, or related field.
• 2+ years of experience developing machine learning or computer vision solutions.
• Experience with Python and modern machine learning frameworks.
• Understanding of deep learning fundamentals, computer vision, and multimodal AI.
• Experience designing experiments and evaluating model performance.
• Strong problem-solving and analytical skills.

Preferred qualifications:

• Experience with Vision Language Models, multimodal AI, or large foundation models.
• Experience with computer vision datasets and annotation pipelines.
• Familiarity with model fine-tuning and evaluation methodologies.
• Experience with cloud-based ML environments such as Azure.
• Understanding of autonomous driving, robotics, or ADAS applications.
• Experience working in cross-functional engineering teams.