A

AMD

Designs chips for computers and AI systems

AI Performance Engineer - Life Sciences

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeNo salary listed

About the Job

Requirements

  • A Master's degree, PhD, or equivalent practical experience in Computer Science, Computer Engineering, Computational Science, Applied Mathematics, Cheminformatics, Bioinformatics, or a related field.
  • Relevant experience developing and delivering machine learning, simulation, or high-performance computing solutions.
  • Experience building machine learning and simulation applications and pipelines.
  • A strong understanding of deep learning architectures, including transformers, diffusion models, and language models.
  • Proficiency in Python and familiarity with C++ in production environments.
  • Software engineering skills across rapid prototyping, debugging, profiling, optimization, and delivery of maintainable production code.
  • Familiarity with AI-assisted development tools such as Claude Code or Cursor.
  • Experience with performance profiling tools such as rocprof, PyTorch Profiler, or Nsight.
  • Experience with framework-level optimization, including compilation techniques and backends such as Inductor or TensorRT.
  • Experience profiling and optimizing GPU workloads using ROCm and/or CUDA.
  • GPU software development experience using technologies such as HIP, CUDA, or OpenCL.
  • Experience developing GPU kernels with HIP, CUDA, or Triton.
  • Experience working in high-performance computing environments.
  • Effective written and verbal communication and presentation skills in English.
  • The role seeks a software engineer with expertise in performance optimization and technical leadership skills to drive complex challenges to resolution.
  • Strong written and verbal communication skills in English.
  • Academic credentials for the secondary GPU Performance Engineer role: Bachelor's or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field, or equivalent practical experience.

Responsibilities

  • Optimize machine learning models through profiling, bottleneck analysis, and efficient mapping of workloads to GPU architectures.
  • Analyze emerging machine learning models, understand their compute and memory requirements, and optimize them for training and inference across hardware platforms.
  • Perform optimization at the kernel, framework, and hardware levels.
  • Work hands-on with deep learning and transformer architectures, as well as physics-based simulation models.
  • Profile and analyze workloads on current hardware, identify performance bottlenecks, and develop strategies to improve efficiency and scalability.
  • Contribute to the Life Sciences workstream within AMD Silo AI's R&D and Models unit as part of an interdisciplinary team.
  • Partner with life sciences AI developers and domain experts to understand requirements and translate them into technical solutions.
  • Share knowledge, experience, and best practices with the wider team through training, mentoring, and collaboration.
  • Collaborate with teams located in Finland and internationally across the organization.
  • Engage with clients to explain performance bottlenecks, optimization opportunities, and proposed solutions.
  • Benchmark, analyze, and optimize key machine learning applications on single- and multi-GPU systems at the kernel, framework, and hardware levels.
  • Design, implement, and test GPU kernels and algorithms for tensor operations, including matrix multiplication and convolutions used in high-performance machine learning libraries and frameworks.
  • Identify architectural opportunities to improve transformer-based and deep learning architectures.
  • Communicate learnings, outcomes, and impact to internal and external stakeholders.
  • Deliver high-quality, maintainable code and documentation aligned with open-source software development best practices.
  • Continuously identify and implement improvements to machine learning kernels for AMD GPUs, focusing on performance and power efficiency.
  • Stay informed about software and hardware trends, particularly in GPU architecture and machine learning algorithms.
  • Improve development workflows and continuous integration infrastructure to enable faster and more reliable delivery.
  • Design and develop GPU and machine learning technologies.
  • Debug and resolve existing issues while researching and implementing more efficient alternatives.
  • Build and maintain technical relationships with internal teams and external partners.

Desired Qualifications

  • Experience with JAX would be beneficial.
  • Experience building and operating large-scale machine learning systems, including training infrastructure and distributed computing environments.
  • Understanding of GPU architectures and low-level optimization techniques, including memory hierarchy, instruction scheduling, and performance trade-offs.
  • Experience developing, maintaining, and supporting open-source software projects, including releases, documentation, and continuous integration pipelines.
  • Experience with cloud platforms such as AWS, GCP, or Azure.
  • Familiarity with cluster orchestration technologies such as Slurm, Kubernetes, or Yarn.
  • Hands-on experience with life sciences foundation models, including OpenFold, AlphaFold, Boltz, ESM, Geneformer, or scGPT families.
  • Hands-on experience with life sciences simulation workloads such as GROMACS or NAMD.
  • A publication record at conferences such as NeurIPS, ICML, ICLR, MLSB, ISMB, or RECOMB.
  • Experience building production-grade agentic AI systems and workflows.
  • Strong understanding of modern GPU architectures.
  • At least 3 years of GPU software development experience using HIP, CUDA, or OpenCL.
  • Experience working directly with hardware instruction set architecture is highly valued.
  • At least 5 years of system-level programming experience in C++ (C++17 or later preferred).
  • Experience with GPU profiling, debugging, benchmarking, and performance analysis tools.
  • Background in high-performance computing or other performance-critical systems.
  • Familiarity with modern machine learning frameworks such as PyTorch and MIOpen.
  • Experience with tile-based programming models and frameworks such as Triton and CUTLASS.

About the Company

AMD

Designs chips for computers and AI systems

AMD designs processors, graphics chips and AI accelerators for computer makers, cloud operators and businesses that build electronic devices. Its Ryzen processors power personal computers, while EPYC processors handle work in data centers and Radeon chips render graphics. Instinct accelerators run AI models, paired with software that developers use to train models and put them into production. AMD sells chips and related computing systems through equipment makers, distributors and retail channels, and contracts with outside foundries to fabricate its semiconductor designs. It was founded in 1969 and has its headquarters in Santa Clara, California.

Company size
10,001+
Company stage
IPO
Headquarters
Santa Clara, CA
Founded
1969

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