Life Sciences AI Research Engineer

Sourced from diversityjobs.com

AMD seeks 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 lets researchers tackle larger and more complex biological problems on AMD platforms. Focus areas include genomics, biological foundation models, protein and molecular modeling, and computational drug discovery.

• Investigate emerging life sciences workloads and their scientific objectives, algorithms, and computational requirements
• Develop, implement, and evaluate algorithms and AI methods for genomics, biomolecular modeling, and related areas
• Profile and optimize scientific applications and AI workflows on AMD platforms
• Explore algorithmic reformulations, parallelization, numerical precision, and GPU acceleration
• Build reproducible benchmarks measuring end-to-end performance and scientific outcomes
• Collaborate with academic and industry partners to translate scientific needs into implementations
• Contribute reusable software and improvements to open-source projects
• Communicate findings through reports, demonstrations, and publications

• Strong computational expertise in a life sciences area such as computational genomics, bioinformatics, protein modeling, computational chemistry, or molecular simulation
• Hands-on experience implementing scientific algorithms or ML models for biological applications
• Strong Python skills plus C++ or another high-performance scientific computing language
• Solid understanding of algorithms, data structures, numerical methods, and experimental evaluation
• Experience profiling, debugging, and improving computational software
• Ability to work independently and collaborate across scientific and engineering disciplines
• Clear written and verbal communication skills

• Biological foundation models, protein structure prediction/design, molecular generative models, or genomic sequence modeling
• Scientific AI architectures such as transformers, geometric deep learning, GNNs, or diffusion models
• GPU programming with HIP, CUDA, or Triton
• PyTorch experience including custom operators or distributed training/inference
• Parallel algorithms, multi-GPU computing, or large-scale scientific workflows
• Research contributions via publications, software, patents, or deployed applications