Job description

Overview Microsoft Research is building next-generation AI systems that connect frontier model behavior with runtime, memory, interconnect, and hardware architecture. You will help define cross-layer architectures that improve sustained inference throughput, accelerator utilization, energy efficiency, and the economics of large-scale AI infrastructure. As a Senior Principal Researcher you will lead research from first-principles workload analysis through architectural modeling, pre-silicon definition, prototype evaluation, and technology transfer. You will work across Microsoft Research, AI infrastructure, systems software, silicon engineering, packaging, and hardware architecture while developing expertise in emerging AI workloads, memory systems, and deployable hardware platforms. The expected work arrangement is three days per week in-office, subject to confirmation and applicable approvals. Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond. Responsibilities • Lead end-to-end AI system, memory, and hardware architecture, identifying cross-layer opportunities across workloads, runtimes, memory systems, and hardware. • Develop architectural abstractions and mechanisms that connect AI model execution behavior with platform capabilities. • Analyze emerging AI workloads, including computation, memory access, communication, data movement, bandwidth, latency, capacity, and power requirements. • Drive pre-silicon architectural exploration, performance modeling, feasibility analysis, and implementation pathfinding for next-generation AI hardware. • Evaluate trade-offs across performance, bandwidth, latency, power, thermal limits, silicon area, packaging, and total cost of ownership. • Build analytical models, simulators, prototypes, and experimental systems to validate architectural hypotheses. • Collaborate across research and product engineering, publish research, mentor technical contributors, and transfer promising concepts toward deployment. Qualifications Required Qualifications: • Doctorate in Computer Science, Computer Engineering, Electrical Engineering, or relevant field AND 6+ years related research experience • OR Master's Degree in Computer Science, Computer Engineering, Electrical Engineering, or relevant field AND 7+ years related research experience • OR Bachelor's Degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field AND 9+ years related research experience • OR equivalent experience. Preferred Qualifications: • Extensive experience in computer architecture, memory systems, silicon architecture, AI systems, platform design, or hardware/software co-design. • Experience reasoning across workload behavior, system software, memory hierarchy, interconnects, and hardware architecture. • Experience with pre-silicon architecture definition, modeling, feasibility analysis, or highly parallel accelerator-based systems. • Record of technical innovation demonstrated through research, patents, publications, prototypes, architecture delivery, or product deployment. • Experience with DRAM, HBM, CXL, GPU memory systems, host-memory interfaces, or high-bandwidth memory architectures. • Experience with accelerator-attached memory, advanced packaging, 2.5D/3D integration, or high-speed scale-up interconnects. • Experience analyzing large AI workloads such as LLM inference or training, mixture-of-experts routing, and KV-cache behavior. • Experience developing performance models, cycle-accurate simulators, prototypes, or performance-analysis infrastructure. • Experience translating new memory or hardware capabilities into measurable system-level improvements. • Experience evaluating bandwidth, latency, capacity, locality, power, thermal, area, and data-movement trade-offs. • Publication or patent record in computer architecture, systems, memory systems, AI infrastructure, or hardware platforms. • Experience collaborating across research, pre-silicon design, and product engineering organizations. #Research Research Sciences IC6 - The typical base pay range for this role across the U.S. is USD $165,600 - $296,400 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $220,800 - $331,200 per year. Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here: careers.microsoft.com This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled. Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.

Requirements

Must-haves

Lead end-to-end AI system, memory, and hardware architecture, identifying cross-layer opportunities across workloads, runtimes, memory systems, and hardware.
Develop architectural abstractions and mechanisms that connect AI model execution behavior with platform capabilities.
Analyze emerging AI workloads, including computation, memory access, communication, data movement, bandwidth, latency, capacity, and power requirements.
Drive pre-silicon architectural exploration, performance modeling, feasibility analysis, and implementation pathfinding for next-generation AI hardware.
Evaluate trade-offs across performance, bandwidth, latency, power, thermal limits, silicon area, packaging, and total cost of ownership.
Build analytical models, simulators, prototypes, and experimental systems to validate architectural hypotheses.
Collaborate across research and product engineering, publish research, mentor technical contributors, and transfer promising concepts toward deployment.
OR Bachelor's Degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field AND 9+ years related research experience
OR equivalent experience.
This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.

Nice-to-haves

Extensive experience in computer architecture, memory systems, silicon architecture, AI systems, platform design, or hardware/software co-design.
Experience reasoning across workload behavior, system software, memory hierarchy, interconnects, and hardware architecture.
Experience with pre-silicon architecture definition, modeling, feasibility analysis, or highly parallel accelerator-based systems.
Record of technical innovation demonstrated through research, patents, publications, prototypes, architecture delivery, or product deployment.
Experience with DRAM, HBM, CXL, GPU memory systems, host-memory interfaces, or high-bandwidth memory architectures.
Experience with accelerator-attached memory, advanced packaging, 2.5D/3D integration, or high-speed scale-up interconnects.
Experience analyzing large AI workloads such as LLM inference or training, mixture-of-experts routing, and KV-cache behavior.
Experience developing performance models, cycle-accurate simulators, prototypes, or performance-analysis infrastructure.
Experience translating new memory or hardware capabilities into measurable system-level improvements.
Experience evaluating bandwidth, latency, capacity, locality, power, thermal, area, and data-movement trade-offs.
Publication or patent record in computer architecture, systems, memory systems, AI infrastructure, or hardware platforms.
Experience collaborating across research, pre-silicon design, and product engineering organizations.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aillm

Platform: eightfold
Effort: medium