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.
COLLABORATION
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 effective 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 clearly and effectively.
MAIN GOALS FOR THE FIRST SIX MONTHS
Benchmark, analyze, and optimize the performance of 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 the performance of transformer-based and deep learning architectures.
Communicate learnings, outcomes, and impact to internal and external stakeholders in a clear, structured, and effective way.
Deliver high-quality, maintainable code and documentation aligned with open-source software development best practices.
WHAT YOU’LL BRING
SKILLS AND QUALIFICATIONS
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.
Experience with PyTorch; familiarity with JAX would be beneficial.
Software engineering skills across rapid prototyping, debugging, profiling, optimization, and the 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.
ADDITIONAL VALUED EXPERIENCE
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.
LOCATIONS
Open to candidates based in Finland and Sweden
We welcome applicants from a wide range of backgrounds and experiences. If you are excited about this opportunity but do not meet every qualification listed, we still encourage you to apply. Your unique skills and perspective could be a valuable addition to our team.
GPU PERFORMANCE ENGINEER - AI KERNELS
THE ROLE
AMD is seeking a software engineer with expertise in performance optimization who is passionate about improving the performance of key AI workloads and benchmarks. You will be part of a highly skilled team working with the latest hardware and software technologies to deliver cutting-edge solutions for AI and GPU computing.
THE PERSON
The ideal candidate is passionate about software engineering and brings the technical leadership skills needed to drive complex challenges to resolution. This person communicates effectively, collaborates successfully across diverse teams, and thrives in a fast-paced, innovative environment.
KEY RESPONSIBILITIES
Continuously identify and implement improvements to machine learning kernels for AMD GPUs, with a focus on performance and power efficiency.
Stay informed about software and hardware trends, particularly in GPU architecture and machine learning algorithms.
Improve development workflows and CI infrastructure to enable faster and more reliable delivery.
Design and develop innovative GPU and machine learning technologies.
Debug and resolve existing issues, while researching and implementing more efficient alternatives.
Build and maintain strong technical relationships with internal teams and external partners.
PREFERRED EXPERIENCE
Strong understanding of modern GPU architectures.
3+ years of GPU software development experience using HIP, CUDA, or OpenCL.
Experience working directly with hardware ISA is highly valued.
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 (HPC) or other performance-critical systems.
Familiarity with modern machine learning frameworks such as PyTorch and MIOpen.
Experience with tile-based programming models and frameworks (e.g., Triton, CUTLASS).
Strong written and verbal communication skills in English.
ACADEMIC CREDENTIALS
Bachelor's or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field, or equivalent practical experience.
SAI
#LI-MH3
#LI-MH3
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.
THE ROLE
We are looking for an AI Performance Engineer, Life Sciences to contribute to the development and optimization of foundation models and agentic AI systems for drug discovery. You will work within a multidisciplinary team of machine learning researchers, computational chemists, and software engineers to deliver AI systems with real-world therapeutic impact. This role offers the opportunity to apply expertise in deep learning performance optimization, collaborate across technical domains, and deliver production-grade software solutions.
MAIN 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 a range of 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.
COLLABORATION
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 effective 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 clearly and effectively.
MAIN GOALS FOR THE FIRST SIX MONTHS
Benchmark, analyze, and optimize the performance of 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 the performance of transformer-based and deep learning architectures.
Communicate learnings, outcomes, and impact to internal and external stakeholders in a clear, structured, and effective way.
Deliver high-quality, maintainable code and documentation aligned with open-source software development best practices.
WHAT YOU’LL BRING
SKILLS AND QUALIFICATIONS
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.
Experience with PyTorch; familiarity with JAX would be beneficial.
Software engineering skills across rapid prototyping, debugging, profiling, optimization, and the 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.
ADDITIONAL VALUED EXPERIENCE
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.
LOCATIONS
Open to candidates based in Finland and Sweden
We welcome applicants from a wide range of backgrounds and experiences. If you are excited about this opportunity but do not meet every qualification listed, we still encourage you to apply. Your unique skills and perspective could be a valuable addition to our team.
GPU PERFORMANCE ENGINEER - AI KERNELS
THE ROLE
AMD is seeking a software engineer with expertise in performance optimization who is passionate about improving the performance of key AI workloads and benchmarks. You will be part of a highly skilled team working with the latest hardware and software technologies to deliver cutting-edge solutions for AI and GPU computing.
THE PERSON
The ideal candidate is passionate about software engineering and brings the technical leadership skills needed to drive complex challenges to resolution. This person communicates effectively, collaborates successfully across diverse teams, and thrives in a fast-paced, innovative environment.
KEY RESPONSIBILITIES
Continuously identify and implement improvements to machine learning kernels for AMD GPUs, with a focus on performance and power efficiency.
Stay informed about software and hardware trends, particularly in GPU architecture and machine learning algorithms.
Improve development workflows and CI infrastructure to enable faster and more reliable delivery.
Design and develop innovative GPU and machine learning technologies.
Debug and resolve existing issues, while researching and implementing more efficient alternatives.
Build and maintain strong technical relationships with internal teams and external partners.
PREFERRED EXPERIENCE
Strong understanding of modern GPU architectures.
3+ years of GPU software development experience using HIP, CUDA, or OpenCL.
Experience working directly with hardware ISA is highly valued.
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 (HPC) or other performance-critical systems.
Familiarity with modern machine learning frameworks such as PyTorch and MIOpen.
Experience with tile-based programming models and frameworks (e.g., Triton, CUTLASS).
Strong written and verbal communication skills in English.
ACADEMIC CREDENTIALS
Bachelor's or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field, or equivalent practical experience.
SAI
#LI-MH3
#LI-MH3
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.
