{"id":4160270,"url":"https://alion.io/job/amd-staff-researcher-machine-learning-systems","title":"Staff Researcher- Machine Learning Systems","company":{"id":93,"name":"AMD","domain":"amd.com","url":"https://alion.io/company/amd","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Jibe","truth_index":{"grade":"A","score":96,"open_postings":17,"ghost_share":0,"stale_share":0.176,"repost_share":0,"time_to_fill_p50_days":49,"computed_at":"2026-11-12T10:38:37Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Singapore"],"countries":["SG"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":8283,"max":11000,"currency":"SGD","period":"month","gross":true,"usd_annual":102756},"salary_estimate":null,"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Machine Learning","optional":false}],"status":"live","first_seen_at":"2026-11-12T00:00:00Z","employer_posted_date":null,"last_verified_at":"2026-11-12T00:00:00Z","board_verified":false,"closed_at":null,"days_open":0,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":0},"description":"Responsibilities\nTHE ROLE:\nAMD is seeking a Member of Technical Staff (Staff Researcher) to research and build next-generation machine learning systems, with an emphasis on large-scale AI inference, performance analysis, and system simulation.\nYou will join the AMD Research and Advanced Development (RAD) group, which creates differentiated technologies, de-risks future systems and products, and seeds new market opportunities. The team investigates machine learning systems, AI infrastructure, computer architecture, and heterogeneous computing across hardware and software.\nTHE PERSON:\nThe ideal candidate is a systems researcher with strong analytical and implementation skills. You will work at the intersection of machine learning, systems, and computer architecture to advance the performance, efficiency, and interactivity of emerging AI workloads. You should be able to formulate open-ended research problems, work across multiple levels of the system stack, and validate ideas through rigorous analysis and experimentation. You should also be comfortable leading technically ambiguous projects, collaborating across teams, and adapting research directions to emerging technical opportunities and product priorities.\nKEY RESPONSIBILITIES:\nThe Staff Researcher will independently define and lead research projects. Responsibilities include:\nDevelop methods for efficient large-scale AI inference, targeting improvements in latency, throughput, cost, and interactivity.\nDevelop simulators, emulators, analytical models, and research prototypes to study end-to-end inference-system behavior, including compute, memory, communication, scheduling, and data center-scale constraints.\nDesign rigorous experiments to benchmark and profile AI workloads, validate models against measured system behavior, and identify bottlenecks, performance trade-offs, and optimization opportunities.\nExplore emerging model architectures, inference algorithms, and deployment strategies, and translate advances in machine learning systems and infrastructure into new research directions.\nCollaborate with internal product and research teams and external research institutes, while communicating findings through technical presentations, publications, and recommendations.\nPREFERRED EXPERIENCE:\nStrong experience, including graduate research, internships, or work in academic or industrial research groups. \nResearch or development experience in machine learning systems, particularly large-scale inference, performance modeling and simulation, distributed systems and networking, computer architecture, hardware–software co-design, or GPU and accelerator programming.\nStrong programming and experimental skills, with experience building research prototypes and using modern machine learning frameworks, GPU software stacks, profiling tools, and quantitative performance analysis.\nACADEMIC CREDENTIALS:\nPh.D. in Computer Engineering, Computer Science, Electrical Engineering, or a closely related field.\nSkills\nInternships, Communication, Profiling, Cost Management, Throughput, System Validation, Inference Engine, Academic Research, GPU Programming, Inference Methods, Computer Systems, Computer Architecture, Analytical Modelling, Scheduling, Collaborate With Internal Team, Generative AI 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