WHAT YOU DO AT AMD CHANGES EVERYTHINGAt AMD, our mission is to build great products that accelerate next-generation computing experiencesfrom AI and data centers, to PCs, gaming and embedded systems. Grounded in a culture of innovation and collaboration, we believe real progress comes from bold ideas, human ingenuity and a shared passion to create something extraordinary. When you join AMD, youll discover the real differentiator is our culture. We push the limits of innovation to solve the worlds most important challengesstriving for execution excellence, while being direct, humble, collaborative, and inclusive of diverse perspectives. Join us as we shape the future of AI and beyond. Together, we advance your career. Role descriptionMain responsibilitiesBuild and ship production AI models and training pipelines for robotics and autonomous systems. Design and optimize large-scale ML workloads across multimodal foundation models (VLMs), vision-language-action (VLA), 3D scene reconstruction (e.g., 3D Gaussian Splatting), perception, and data curation. Establish scalable, reproducible playbooks for the full lifecycle: containerized environments, data and training pipelines, evaluation, deployment/serving, and maintainable documentation. Contribute to and maintain open-source codebases, tooling, and reference implementations to accelerate adoption and collaboration. Collaboration with othersAI engineering teams: Identify and integrate state-of-the-art perception models, profile and optimize performance, and publish reusable workload playbooks. Product and software engineers: Co-design and integrate AI workloads into shipped products; support CI/CD validation and provide actionable feedback on product architecture and interfaces. Internal and external partners: Align technical roadmaps with academic and industrial partners; translate requirements into implementable plans and milestones. Cross-site collaboration: Work effectively with product and research teams across Europe and globally, ensuring clear ownership, tight feedback loops, and predictable delivery. Main goals for first 6 monthsRamp up on the codebase, infrastructure, and end-to-end pipelines. Review the domain and produce an implementation-oriented state-of-the-art survey that translates directly into engineering priorities and a roadmap. Deliver and release an end-to-end prototype for a perception or autonomous-systems model (training, evaluation, and deployment path). Lead client-facing implementation work to surface concrete product requirements and translate them into actionable technical deliverables. Ideal candidate profileRequired Skills And QualificationsMS/PhD in ML, Robotics, or related field, or 3+ years of relevant industry experience. Experience building robotics/perception pipelines or components of autonomous-systems stacks. Practical experience profiling and optimizing GPU workloads (ROCm and/or CUDA). Strong grounding in modern deep learning for perception and decision-making (e.g., transformers, CNNs, diffusion models, reinforcement learning). Hands-on experience with vision and multimodal models and tooling (e.g., ViT, CLIP, DINO, LLaVA, diffusion models). Proficiency in Python and familiarity in C++ for production development. Strong experience with PyTorch (bonus: JAX). Strong engineering skills: rapid prototyping, debugging, performance awareness, and shipping maintainable code. Excellent written and spoken English communication. Bonus PointsHands-on experience building and operating large-scale ML systems, including training infrastructure and distributed compute. Strong publication record in leading venues (e.g., CVPR, ICCV, ECCV, NeurIPS, ICRA, IROS). Experience shipping and maintaining open-source projects (releases, docs, CI). Experience with cloud platforms (AWS/GCP/Azure) and cluster orchestration/management (e.g., Slurm, Kubernetes, Yarn).