{"id":2910160,"url":"https://alion.io/job/amd-ai-performance-engineer-life-sciences","title":"AI Performance Engineer (Life Sciences)","company":{"id":93,"name":"AMD","domain":"amd.com","url":"https://alion.io/company/amd","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Jibe","truth_index":{"grade":"B","score":80,"open_postings":157,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":15,"computed_at":"2026-10-22T06:10:10Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":["Helsinki, Finland"],"countries":["FI"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":56000,"max_usd":145000,"period":"year","method":"role_country_seniority_unknown","sample_n":20},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"C++","optional":false},{"name":"CI/CD","optional":false},{"name":"Claude Code","optional":false},{"name":"CUDA","optional":false},{"name":"Cursor","optional":false},{"name":"Diffusion Models","optional":false},{"name":"GCP","optional":false},{"name":"JAX","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"OpenCL","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"ROCm","optional":false},{"name":"SLURM","optional":false},{"name":"TensorRT","optional":false},{"name":"Triton","optional":false}],"status":"live","first_seen_at":"2026-10-08T14:54:00Z","employer_posted_date":"2026-10-20","last_verified_at":"2026-10-22T15:30:46Z","board_verified":true,"closed_at":null,"days_open":14,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":14},"description":"ADVANCE YOUR CAREER. ADVANCE THE WORLD.\nAt AMD, we believe technology has the power to solve the world’s most important challenges. From advancing healthcare and scientific discovery to powering AI and the technologies people rely on every day, innovation at AMD is shaping the future.\nWhether you’re designing next-gen processors, enabling AI breakthroughs, or bringing leading edge products to market, every role at AMD contributes to something bigger - technology that moves the world forward. Join us and, together, we’ll advance your career.\nTHE ROLE\nWe 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.\nMAIN RESPONSIBILITIES\nOptimize machine learning models through profiling, bottleneck analysis, and efficient mapping of workloads to GPU architectures.\nAnalyze emerging machine learning models, understand their compute and memory requirements, and optimize them for training and inference across a range of hardware platforms.\nPerform optimization at the kernel, framework, and hardware levels.\nWork hands-on with deep learning and transformer architectures, as well as physics-based simulation models.\nProfile and analyze workloads on current hardware, identify performance bottlenecks, and develop strategies to improve efficiency and scalability.\nCOLLABORATION\nContribute to the Life Sciences workstream within AMD Silo AI’s R&D and Models unit as part of an interdisciplinary team.\nPartner with life sciences AI developers and domain experts to understand requirements and translate them into effective technical solutions.\nShare knowledge, experience, and best practices with the wider team through training, mentoring, and collaboration.\nCollaborate with teams located in Finland and internationally across the organization.\nEngage with clients to explain performance bottlenecks, optimization opportunities, and proposed solutions clearly and effectively.\nMAIN GOALS FOR THE FIRST SIX MONTHS\nBenchmark, analyze, and optimize the performance of key machine learning applications on single and multi-GPU systems at the kernel, framework, and hardware levels.\nDesign, implement, and test GPU kernels and algorithms for tensor operations, including matrix multiplication and convolutions used in high-performance machine learning libraries and frameworks.\nIdentify architectural opportunities to improve the performance of transformer-based and deep learning architectures.\nCommunicate learnings, outcomes, and impact to internal and external stakeholders in a clear, structured, and effective way.\nDeliver high-quality, maintainable code and documentation aligned with open-source software development best practices.\nWHAT YOU’LL BRING\nSKILLS AND QUALIFICATIONS\nA Master’s degree, PhD, or equivalent practical experience in Computer Science, Computer Engineering, Computational Science, Applied Mathematics, Cheminformatics, Bioinformatics, or a related field.\nRelevant experience developing and delivering machine learning, simulation, or high-performance computing solutions.\nExperience building machine learning and simulation applications and pipelines.\nA strong understanding of deep learning architectures, including transformers, diffusion models, and language models.\nProficiency in Python and familiarity with C++ in production environments.\nExperience with PyTorch; familiarity with JAX would be beneficial.\nSoftware engineering skills across rapid prototyping, debugging, profiling, optimization, and the delivery of maintainable production code.\nFamiliarity with AI-assisted development tools, such as Claude Code or Cursor.\nExperience with performance profiling tools such as rocprof, PyTorch Profiler, or Nsight.\nExperience with framework-level optimization, including compilation techniques and backends such as Inductor or TensorRT.\nExperience profiling and optimizing GPU workloads using ROCm and/or CUDA.\nGPU software development experience using technologies such as HIP, CUDA, or OpenCL.\nExperience developing GPU kernels with HIP, CUDA, or Triton.\nExperience working in high-performance computing environments.\nEffective written and verbal communication and presentation skills in English.\nADDITIONAL VALUED EXPERIENCE\nExperience building and operating large-scale machine learning systems, including training infrastructure and distributed computing environments.\nUnderstanding of GPU architectures and low-level optimization techniques, including memory hierarchy, instruction scheduling, and performance trade-offs.\nExperience developing, maintaining, and supporting open-source software projects, including releases, documentation, and continuous integration pipelines.\nExperience with cloud platforms such as AWS, GCP, or Azure.\nFamiliarity with cluster orchestration technologies such as Slurm, Kubernetes, or Yarn.\nHands-on experience with life sciences foundation models, including OpenFold, AlphaFold, Boltz, ESM, Geneformer, or scGPT families.\nHands-on experience with life sciences simulation workloads such as GROMACS or NAMD.\nA publication record at conferences such as NeurIPS, ICML, ICLR, MLSB, ISMB, or RECOMB.\nExperience building production-grade agentic AI systems and workflows.\nLOCATIONS\nOpen to candidates based in Finland and Sweden\nWe 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.\nBenefits offered are described: AMD benefits at a glance.\nAMD 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.\nAMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD’s “Responsible AI Policy” is available here.\n\nThis posting is for an existing vacancy.\nTHE ROLE\nWe 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.\nMAIN RESPONSIBILITIES\nOptimize machine learning models through profiling, bottleneck analysis, and efficient mapping of workloads to GPU architectures.\nAnalyze emerging machine learning models, understand their compute and memory requirements, and optimize them for training and inference across a range of hardware platforms.\nPerform optimization at the kernel, framework, and hardware levels.\nWork hands-on with deep learning and transformer architectures, as well as physics-based simulation models.\nProfile and analyze workloads on current hardware, identify performance bottlenecks, and develop strategies to improve efficiency and scalability.\nCOLLABORATION\nContribute to the Life Sciences workstream within AMD Silo AI’s R&D and Models unit as part of an interdisciplinary team.\nPartner with life sciences AI developers and domain experts to understand requirements and translate them into effective technical solutions.\nShare knowledge, experience, and best practices with the wider team through training, mentoring, and collaboration.\nCollaborate with teams located in Finland and internationally across the organization.\nEngage with clients to explain performance bottlenecks, optimization opportunities, and proposed solutions clearly and effectively.\nMAIN GOALS FOR THE FIRST SIX MONTHS\nBenchmark, analyze, and optimize the performance of key machine learning applications on single and multi-GPU systems at the kernel, framework, and hardware levels.\nDesign, implement, and test GPU kernels and algorithms for tensor operations, including matrix multiplication and convolutions used in high-performance machine learning libraries and frameworks.\nIdentify architectural opportunities to improve the performance of transformer-based and deep learning architectures.\nCommunicate learnings, outcomes, and impact to internal and external stakeholders in a clear, structured, and effective way.\nDeliver high-quality, maintainable code and documentation aligned with open-source software development best practices.\nWHAT YOU’LL BRING\nSKILLS AND QUALIFICATIONS\nA Master’s degree, PhD, or equivalent practical experience in Computer Science, Computer Engineering, Computational Science, Applied Mathematics, Cheminformatics, Bioinformatics, or a related field.\nRelevant experience developing and delivering machine learning, simulation, or high-performance computing solutions.\nExperience building machine learning and simulation applications and pipelines.\nA strong understanding of deep learning architectures, including transformers, diffusion models, and language models.\nProficiency in Python and familiarity with C++ in production environments.\nExperience with PyTorch; familiarity with JAX would be beneficial.\nSoftware engineering skills across rapid prototyping, debugging, profiling, optimization, and the delivery of maintainable production code.\nFamiliarity with AI-assisted development tools, such as Claude Code or Cursor.\nExperience with performance profiling tools such as rocprof, PyTorch Profiler, or Nsight.\nExperience with framework-level optimization, including compilation techniques and backends such as Inductor or TensorRT.\nExperience profiling and optimizing GPU workloads using ROCm and/or CUDA.\nGPU software development experience using technologies such as HIP, CUDA, or OpenCL.\nExperience developing GPU kernels with HIP, CUDA, or Triton.\nExperience working in high-performance computing environments.\nEffective written and verbal communication and presentation skills in English.\nADDITIONAL VALUED EXPERIENCE\nExperience building and operating large-scale machine learning systems, including training infrastructure and distributed computing environments.\nUnderstanding of GPU architectures and low-level optimization techniques, including memory hierarchy, instruction scheduling, and performance trade-offs.\nExperience developing, maintaining, and supporting open-source software projects, including releases, documentation, and continuous integration pipelines.\nExperience with cloud platforms such as AWS, GCP, or Azure.\nFamiliarity with cluster orchestration technologies such as Slurm, Kubernetes, or Yarn.\nHands-on experience with life sciences foundation models, including OpenFold, AlphaFold, Boltz, ESM, Geneformer, or scGPT families.\nHands-on experience with life sciences simulation workloads such as GROMACS or NAMD.\nA publication record at conferences such as NeurIPS, ICML, ICLR, MLSB, ISMB, or RECOMB.\nExperience building production-grade agentic AI systems and workflows.\nLOCATIONS\nOpen to 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