AI Engineer - Robotics AD & Optimization

WHAT YOU DO AT AMD CHANGES EVERYTHING At AMD, our mission is to build great products that accelerate next-generation computing experiences—from 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, you’ll discover the real differentiator is our culture. We push the limits of innovation to solve the world’s most important challenges—striving 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 description Main responsibilities Build 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 others AI 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 months Ramp 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 profile Required Skills and Qualifications MS/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 Points Hands-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). #LI-MH3 #LI-HYBRID Benefits offered are described: AMD benefits at a glance. AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process. AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD’s “Responsible AI Policy” is available here. This posting is for an existing vacancy.

Benefits offered are described: AMD benefits at a glance. AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process. AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD’s “Responsible AI Policy” is available here. This posting is for an existing vacancy.

Role description Main responsibilities Build 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 others AI 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 months Ramp 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 profile Required Skills and Qualifications MS/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 Points Hands-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). #LI-MH3 #LI-HYBRID