Life Sciences AI Research Engineer

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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.

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.

• 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.

• 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…