VLM Research Engineer (m/f/d)
External
Deltia · Onsite BerlinPrepare for this interview
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Requirements
- Publications at top-tier venues (CVPR, ICCV, ECCV, NeurIPS, ICLR, etc.) on video, multimodal learning or scene understanding
- Experience with 3D/4D scene representations, action generation or embodied / sense-plan-act style projects
- Inference optimisation: quantisation, TensorRT, model distillation, or deployment on constrained hardware
- Prior experience in a startup or applied research lab environment
Benefits
Additional Information
We're looking for a Research Engineer to push the limits of vision-language models for real-world video understanding. You'll work on applied, state-of-the-art multimodal models and turn them into production pipelines used by customers. Your role Design and adapt vision-language and video models for scene understanding, temporal reasoning and activity / action recognition Build and maintain large-scale training and evaluation pipelines on GPU clusters Curate and augment video-text and action datasets, including synthetic labels and retrieval-based augmentation Develop robust benchmarks for video QA, instruction following and temporal understanding, and use them to drive iterative model improvements Cut and refactor model architectures for efficiency and deployability (compression, pruning, distillation) Deliver production-ready inference pipelines to product and customer teams, working closely with CV, platform and robotics engineers You bring Completed PhD (or equivalent research track record) in computer vision, machine learning, robotics or a related field Strong background in video-centric deep learning: scene understanding, temporal / activity / action recognition, or video generation Experience training and adapting large vision or VLM models (e.g. InternVL, Qwen-VL, DeepSeek-VL, similar stacks) Proven work with multi-GPU training (PyTorch, distributed, mixed precision) and large-scale datasets Solid engineering habits: clean Python, reproducible experiments, reliable data and training pipelines Track record of moving research into usable systems (demos, internal tools, or productised features) in fast-moving teams
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Company Intel
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