Hybrid Machine Learning Engineer, Multimodal Models – LLM, VLM

Posted 2 weeks ago

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About the role

  • Contribute to dataset curation activities - collecting, cleaning, labeling, and preparing multimodal data for training and validation.
  • Train and fine-tune LLMs, VLMs, and VLA models to interpret visual scenes and produce actionable navigation insights supporting autonomous vehicle decision-making.
  • Support validation of multimodal models - evaluating vision-language-action behavior and helping identify performance gaps across driving scenarios.
  • Collaborate closely with AV planners, perception teams, and infrastructure engineers to ensure seamless deployment in a real-time ecosystem.
  • You’ll have the opportunity to influence the strategic direction of language-driven autonomy - proposing new ideas, shaping model capabilities, and driving innovation from research to real-world deployment.

Requirements

  • M.Sc. in Deep Learning, Computer Vision, NLP, or a related field (Ph.D. an advantage).
  • Hands-on experience in developing deep learning models.
  • Strong programming skills in Python (additional C++ is an advantage).
  • Experience with modern DL frameworks (e.g., PyTorch, TensorFlow).
  • Experience with large multimodal or language models (LLMs/VLMs/VLA models) and their real-world integration - advantage.

Job title

Machine Learning Engineer, Multimodal Models – LLM, VLM

Job type

Experience level

Mid levelSenior

Salary

Not specified

Degree requirement

Postgraduate Degree

Location requirements

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