Description
WHAT YOU DO AT AMD CHANGES EVERYTHING
We care deeply about transforming lives with AMD technology to enrich our industry, our communities, and the world. Our mission is to build great products that accelerate next-generation computing experiences – the building blocks for the data center, artificial intelligence, PCs, gaming and embedded. Underpinning our mission is the AMD culture. We push the limits of innovation to solve the world's most important challenges. We strive for execution excellence while being direct, humble, collaborative, and inclusive of diverse perspectives.
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Role description:
Lead HPC and AI for science initiatives to accelerate scientific discovery in domains such as climate modeling, materials design, drug discovery, quantum systems, and fluid dynamics. Integrate State-of-The-Art AI methods—including foundation models, surrogate modeling and agentic AI frameworks—into large-scale HPC workflows. Architect reproducible pipelines, optimize performance on heterogeneous clusters, and ensure ease-of-use and scalability across thousands of CPU/GPU cores. Build collaborations with scientific teams, funding bodies, and external partners to transform cutting-edge AI research into practical, high-impact scientific tools.
Main responsibilities:
Identify, implement and benchmark scientific AI models: Evaluate and adopt foundation models, surrogate models, and agentic AI techniques for HPC applications in scientific domains such as climate, materials, chemistry, and fluid dynamics.
Tune and optimize HPC workloads: Accelerate large-scale AI model training/inference and scientific simulations on heterogeneous clusters via mixed precision, distributed strategies, scheduling, performance tuning, etc.
Develop reproducible scientific AI playbooks: Create scalable pipelines covering data ingestion, preprocessing, containerization, training, inference, serving, and documentation.
Collaborate with domain scientists and engineers: Integrate AI into simulations and experiments; co-design scientific workflows and enhance productivity across HPC centers.
Drive funding and partnerships: Identify grants, co-author proposals, and establish internal and external collaborations to advance AI for science in HPC.
Collaboration with Others:
AI Engineer Teams: Integrate scientific AI models, conduct profiling and optimization, and author reproducible AI/HPC workload playbooks.
Scientists & Domain Experts: Partner on AI-enabled simulations and workflows in physics, chemistry, materials, climate, and quantum sciences.
Product & Software Engineers: Co-design pipelines and provide feedback on infrastructure usability, CI/CD integration, and reproducibility.
Hardware Teams: Understand accelerator features, provide feedback on drivers and firmware, validate performance metrics, and suggest hardware optimizations.
External & Internal Partners: Establish co-innovation initiatives, pursue joint grants, and co-author proposals with academia, labs, and industry.
Main goals for first 6 months:
Get up to speed on HPC infrastructure, scientific workloads, and existing AI/HPC pipelines.
Review state-of-the-art AI for science methods (foundation, surrogate, physics-informed, agentic).
Deliver a scalable pilot pipeline for a flagship use case (e.g., climate modeling or materials discovery).
Demonstrate measurable performance improvements (≥ 5%) in HPC workloads using AI-enabled pipelines.
Required Skills and Qualifications:
Master's or PhD in Computer Science, Computational Science, Physics, Chemistry, or other fields with focus on AI/ML for scientific applications.
Strong skills in Python and scientific computing. Hands‑on experience with deep‑learning frameworks (PyTorch/JAX/TensorFlow/ONNX).
Knowledge of HPC cluster architectures, high‑performance networks and job schedulers that distribute workloads across nodes.
Knowledge of HPC libraries (MPI, OpenMP, CUDA/HIP) and orchestration tools (Slurm, Kubernetes, Yarn).
Expertise in distributed AI model training/inference frameworks (Megatron, DeepSpeed, vLLM, HF Accelerate, XDiT).
Knowledge of runtime profiling and performance optimization (CUDA/HIP, Triton, XLA, cuBLASLt/hipBLASLt, Graph/GEMM optimization).
Fluent in English, with strong written and spoken communication skills.
Bonus Points:
Publications in top-tier AI or scientific journals (NeurIPS, ICML, Nature, Science).
Experience designing or operating large-scale AI/HPC platforms or scientific AI services.
Experience implementing AI agents that can set goals, plan and reason, and coordinate specialized tools. Familiarity with multi‑agent orchestration and memory management.
Knowledge of GPU programming (CUDA/HIP/OpenCL) and accelerator hardware.
Experience with scientific AI frameworks (Aurora, MatterGen, PhysicsNemo)
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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.
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