We value depth in one or more relevant areas, curiosity across disciplines, and the ability to take ownership of a technical project from investigation to validated results.
KEY RESPONSIBILITIES
Investigate important and emerging life sciences workloads, understanding their scientific objectives, algorithms, computational requirements, and practical limitations.
Develop, implement, and evaluate algorithms and AI methods for applications in genomics, biomolecular modeling, or related areas.
Profile and optimize scientific applications and AI workflows on AMD platforms, addressing bottlenecks in computation, memory, data processing, and communication.
Explore algorithmic reformulations, parallelization strategies, numerical precision, and GPU acceleration while preserving scientific validity.
Build reproducible benchmarks that measure end-to-end performance, scalability, resource use, and relevant scientific outcomes.
Work directly with academic and industry collaborators to translate scientific needs into technical requirements and working implementations.
Contribute reusable software, optimized operators, and improvements to relevant open-source projects.
Translate experimental findings into recommendations for AMD’s research priorities, software capabilities, and hardware requirements.
Communicate results through technical reports, demonstrations, publications, and invention disclosures, as appropriate.
REQUIRED EXPERIENCE
Strong computational expertise in at least one life sciences area, such as computational genomics, bioinformatics, protein modeling, computational chemistry, or molecular simulation.
Hands-on experience implementing scientific algorithms or developing machine learning models for biological applications.
Strong programming skills in Python and experience with C++ or another language used for high-performance scientific computing.
Solid understanding of algorithms, data structures, numerical methods, and experimental evaluation.
Experience profiling, debugging, and improving computational software.
Ability to work independently on a defined technical problem and collaborate effectively across scientific and engineering disciplines.
Clear written and verbal communication skills.
PREFERRED EXPERIENCE
Biological foundation models, protein structure prediction or design, molecular generative models, or genomic sequence modeling.
Scientific AI architectures, including transformers, geometric deep learning, graph neural networks, or diffusion models.
GPU programming and optimization using HIP, CUDA, Triton, or comparable technologies.
Deep learning frameworks such as PyTorch, including custom operators or distributed training and inference.
Parallel algorithms, multi-GPU computing, or large-scale scientific workflows.
Established life sciences software ecosystems and experience extending or contributing to open-source tools.
Research contributions demonstrated through publications, software, patents, or deployed scientific applications.
ACADEMIC CREDENTIALS
Master’s, or PhD in Computational Biology, Bioinformatics, Computer Science, Applied Mathematics, Computational Chemistry, or a related discipline, or equivalent practical experience preferred.
LOCATION: Longmont, CO; Austin, TX and Santa Clara, Ca are options.
This role is not eligible for visa sponsorship.
#LI-MR1
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
AMD is looking for a hands-on research engineer to advance life sciences computing at the intersection of computational biology, AI, and high-performance computing.
You will investigate emerging scientific workloads, develop and optimize algorithms, and build software that enables researchers to tackle larger and more complex biological problems on AMD platforms. Areas of interest include genomics, biological foundation models, protein and molecular modeling, and computational drug discovery.
Working with AMD researchers, software engineers, and external collaborators, you will take projects from scientific problem definition through implementation and evaluation. Your findings will help identify where AMD can deliver meaningful advances in life sciences computing and guide future research and software investments.
THE PERSON
You bring deep understanding of a computational life sciences domain and the programming skills to turn scientific ideas into working software. You enjoy writing code, testing hypotheses, and solving difficult technical problems. You can distinguish improvements that matter to a scientific workflow from gains in an isolated benchmark and communicate your findings clearly to both domain scientists and systems engineers.
We value depth in one or more relevant areas, curiosity across disciplines, and the ability to take ownership of a technical project from investigation to validated results.
KEY RESPONSIBILITIES
Investigate important and emerging life sciences workloads, understanding their scientific objectives, algorithms, computational requirements, and practical limitations.
Develop, implement, and evaluate algorithms and AI methods for applications in genomics, biomolecular modeling, or related areas.
Profile and optimize scientific applications and AI workflows on AMD platforms, addressing bottlenecks in computation, memory, data processing, and communication.
Explore algorithmic reformulations, parallelization strategies, numerical precision, and GPU acceleration while preserving scientific validity.
Build reproducible benchmarks that measure end-to-end performance, scalability, resource use, and relevant scientific outcomes.
Work directly with academic and industry collaborators to translate scientific needs into technical requirements and working implementations.
Contribute reusable software, optimized operators, and improvements to relevant open-source projects.
Translate experimental findings into recommendations for AMD’s research priorities, software capabilities, and hardware requirements.
Communicate results through technical reports, demonstrations, publications, and invention disclosures, as appropriate.
REQUIRED EXPERIENCE
Strong computational expertise in at least one life sciences area, such as computational genomics, bioinformatics, protein modeling, computational chemistry, or molecular simulation.
Hands-on experience implementing scientific algorithms or developing machine learning models for biological applications.
Strong programming skills in Python and experience with C++ or another language used for high-performance scientific computing.
Solid understanding of algorithms, data structures, numerical methods, and experimental evaluation.
Experience profiling, debugging, and improving computational software.
Ability to work independently on a defined technical problem and collaborate effectively across scientific and engineering disciplines.
Clear written and verbal communication skills.
PREFERRED EXPERIENCE
Biological foundation models, protein structure prediction or design, molecular generative models, or genomic sequence modeling.
Scientific AI architectures, including transformers, geometric deep learning, graph neural networks, or diffusion models.
GPU programming and optimization using HIP, CUDA, Triton, or comparable technologies.
Deep learning frameworks such as PyTorch, including custom operators or distributed training and inference.
Parallel algorithms, multi-GPU computing, or large-scale scientific workflows.
Established life sciences software ecosystems and experience extending or contributing to open-source tools.
Research contributions demonstrated through publications, software, patents, or deployed scientific applications.
ACADEMIC CREDENTIALS
Master’s, or PhD in Computational Biology, Bioinformatics, Computer Science, Applied Mathematics, Computational Chemistry, or a related discipline, or equivalent practical experience preferred.
LOCATION: Longmont, CO; Austin, TX and Santa Clara, Ca are options.
This role is not eligible for visa sponsorship.
#LI-MR1
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.
