Job descriptionWhat's the role?: We are seeking an experienced Lead Data Scientist to drive the development of advanced Vision Foundation Models, Vision-Language Models, and multimodal AI systems for large-scale image and video understanding. You will lead the design, development, and deployment of scalable AI solutions, translating cutting-edge research into real-world applications. Key Responsibilities - Design, train, and optimize large-scale vision foundation models across image and video modalities - Develop multimodal AI systems using architectures such as Vision Transformers (ViT), SAM, DINOv3, CLIP, and VLMs - Apply self-supervised learning, transfer learning, and fine-tuning approaches for downstream tasks - Build and enhance Vision-Language Models for visual reasoning and multimodal understanding - Develop Retrieval-Augmented Generation (RAG) pipelines and multimodal knowledge retrieval systems - Work with embeddings, vector databases, and semantic search frameworks - Build scalable pipelines for training, evaluation, and deployment - Manage large-scale image, video, and multimodal datasets - Optimize distributed training workflows and model performance - Translate research into production-ready solutions and explore emerging approaches in multimodal AI and generative AI - Evaluate model quality, robustness, and retrieval effectiveness Who are you?: You bring strong expertise in computer vision, foundation models, and multimodal AI systems, along with the ability to deliver scalable solutions from research to production. - Master’s or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field - Extensive experience in deep learning, computer vision, or multimodal AI - Strong programming skills in Python and experience with PyTorch - Deep understanding of computer vision, Vision Transformers, self-supervised learning, Vision-Language Models, and multimodal systems - Hands-on experience with foundation models such as SAM, DINOv3, CLIP, BLIP/BLIP-2, LLaVA, or diffusion-based vision models - Experience building RAG pipelines, semantic retrieval systems, and working with embeddings and vector databases such as FAISS, Milvus, Pinecone, or Weaviate - Experience working with large-scale image and video datasets and distributed training environments - Familiarity with GPU acceleration and scalable ML infrastructure - Exposure to generative AI, multimodal reasoning systems, or large-scale perception systems - Contributions to research, publications, or open-source projects are valued --- What Do We Offer? - Opportunity to work on cutting-edge AI and multimodal technologies - A collaborative, inclusive, and innovation-driven work environment - Opportunities to learn, grow, and advance your career - Exposure to large-scale, real-world AI challenges and global impact - Competitive compensation and performance-based bonus - Flexible and hybrid working options - Employee wellness programs and professional development support HERE Technologies is an equal opportunity employer. All qualified applicants will receive consideration without regard to race, color, religion, gender, gender identity, sexual orientation, age, or disability. Who are we?: HERE Technologies is a location data and technology platform company. We empower our customers to achieve better outcomes – from helping a city manage its infrastructure or a business optimize its assets to guiding drivers to their destination safely. At HERE we take it upon ourselves to be the change we wish to see. We create solutions that fuel innovation, provide opportunity and foster inclusion to improve people’s lives. If you are inspired by an open world and driven to create positive change, join us. Learn more about us on our YouTube Channel. About the Team You will be part of a highly collaborative AI/ML team focused on developing next-generation Vision Foundation Models (VFMs), Vision-Language Models (VLMs), and multimodal AI systems. The team works at the intersection of research and scalable production systems, driving innovation in large-scale image and video understanding.
