Job description

Are you the kind of engineer who obsesses over performance? Does the idea of hunting for hidden waste across the world's largest infrastructure and turning those findings into hundreds of millions of dollars in freed capacity sound like the most exciting job you can imagine? If so, keep reading. This role offers something rare. Deep technical research freedom, immediate access to the largest compute infrastructure in the world, and direct line of sight to impact that reshapes how Amazon builds and operates its infrastructure. The Efficiency Red Team has already driven billions in cumulative capacity savings, reclaimed petabytes of DRAM through automated profiling, and built GPU efficiency frameworks adopted across Amazon. And we are just getting started. The surface area of opportunity grows with every new workload, every new chip generation, and every new AI model Amazon deploys. You would be joining a team with a proven foundation and an expanding frontier. Compute demand is rising with no sign of slowing down. The explosive growth of Generative AI has created unprecedented demand for GPUs, and the shock-waves are now constraining DRAM availability for traditional compute across the entire industry. Silicon, power, and physical space are not infinite. When capacity is constrained, efficiency becomes the new capacity. At Amazon's scale, a single efficiency pattern discovered and applied can unlock resources equivalent to building entirely new data centers. A modest reduction in memory footprint across a widely deployed library translates into millions of dollars in freed capacity. An optimization that improves response latency by milliseconds across billions of requests directly improves the experience of hundreds of millions of customers and drives business growth. We are looking for experienced performance engineers to join a focused group whose mission is discovering and eliminating waste across every layer of the compute stack. You will work at the intersection of hardware and software, from GPU memory management in generative AI inference pipelines to DRAM allocation patterns deep inside language runtime, from storage subsystem inefficiencies to CPU scheduling behaviors at hyper-scale. Key job responsibilities - Apply first-principles analysis across CPU, DRAM, GPU, storage, I/O, and networking layers to uncover optimization opportunities that others overlook - Design and lead rigorous investigations that establish root causes and produce actionable findings with broad impact - Build proof-of-concept implementations for novel efficiency approaches such as memory compression, smart over-subscription, and language-level rewrites - Develop repeatable patterns and practices that translate individual findings into fleet-wide optimization playbooks - Quantify business impact at Amazon scale, connecting every technical insight to capacity freed, dollars saved, and customer experience improved - Collaborate across organizational boundaries where the largest opportunities often live at the intersection of teams, systems, and technology stacks - Contribute findings to internal profiling tools, automated optimization agents, and engineering guidance that become force multipliers across Amazon - Directly influence how Amazon reasons about performance engineering and capacity efficiency at scale as a founding member of the Efficiency Red Team A day in the life Your day could include profiling a widely deployed library to quantify its memory allocation overhead, then building a prototype that demonstrates a significant reduction in DRAM footprint and calculating the impact in freed servers and dollars. You might be deep in GPU utilization data, investigating why a generative AI training cluster shows substantial idle time during peak hours and designing an over-subscription model that safely reclaims that capacity. You could be translating complex performance findings into a clear narrative for senior leadership, showing them that a single optimization pattern applied across Amazon's fleet frees capacity worth more than most companies spend on infrastructure in a year. Some weeks you will be reading CPU performance counters and cache miss rates. Other weeks you will be analyzing token throughput economics in large language model serving infrastructure. The variety is deliberate because waste hides everywhere, and finding it requires curiosity that refuses to stay in a single lane. About the team The Efficiency Red Team is a small group of performance engineers who move fluidly across the entire compute stack. The same engineer might profile CPU cache behavior in a traditional workload one week and analyze token throughput in a GPU inference cluster the next. You will have the freedom to pursue deep technical research, choose your own investigations, and follow the data wherever it leads. This is a founding opportunity to help shape the mission, methods, and culture of a team designed to be Amazon's center of excellence in performance engineering. Every pattern this team discovers feeds directly into detection and remediation tools that operate continuously across the fleet, turning individual insights into lasting, compounding impact. - 7+ years of non-internship professional software development experience - 7+ years of programming with at least one software programming language experience - 5+ years of leading design or architecture (design patterns, reliability and scaling) of new and existing systems experience - Experience as a mentor, tech lead or leading an engineering team - 7+ years of professional experience in systems programming, performance engineering, or infrastructure optimization - Deep proficiency in at least one systems language (C, C++, Rust, or Java) - Hands-on experience with performance profiling and analysis tools - Demonstrated ability to conduct root cause analysis across multiple layers of the compute stack (CPU, memory, storage, networking) - Track record of delivering measurable performance improvements in production systems at scale - Bachelor's degree in computer science or equivalent - Experience with GPU profiling and optimization (CUDA, GPU memory management, inference pipeline tuning) - Familiarity with generative AI infrastructure including model serving, training pipelines, and token economics - Experience with language runtime internals (JVM garbage collection tuning, memory management design, or equivalent) - Background in capacity planning, fleet management, or infrastructure economics at hyper-scale - Contributions to open source performance tools or published research in systems performance Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company’s reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit amazon.jobs for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner. The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at amazon.jobs . USA, CA, Sunnyvale - 193,300.00 - 261,500.00 USD annually USA, NY, New York - 184,900.00 - 250,200.00 USD annually USA, WA, Seattle - 168,100.00 - 227,400.00 USD annually

Requirements

Must-haves

Key job responsibilities
Apply first-principles analysis across CPU, DRAM, GPU, storage, I/O, and networking layers to uncover optimization opportunities that others overlook
Design and lead rigorous investigations that establish root causes and produce actionable findings with broad impact
Build proof-of-concept implementations for novel efficiency approaches such as memory compression, smart over-subscription, and language-level rewrites
Develop repeatable patterns and practices that translate individual findings into fleet-wide optimization playbooks
Quantify business impact at Amazon scale, connecting every technical insight to capacity freed, dollars saved, and customer experience improved
Collaborate across organizational boundaries where the largest opportunities often live at the intersection of teams, systems, and technology stacks
Contribute findings to internal profiling tools, automated optimization agents, and engineering guidance that become force multipliers across Amazon
Directly influence how Amazon reasons about performance engineering and capacity efficiency at scale as a founding member of the Efficiency Red Team
A day in the life
About the team
7+ years of non-internship professional software development experience
7+ years of programming with at least one software programming language experience
5+ years of leading design or architecture (design patterns, reliability and scaling) of new and existing systems experience
Experience as a mentor, tech lead or leading an engineering team
7+ years of professional experience in systems programming, performance engineering, or infrastructure optimization
Deep proficiency in at least one systems language (C, C++, Rust, or Java)
Hands-on experience with performance profiling and analysis tools
Demonstrated ability to conduct root cause analysis across multiple layers of the compute stack (CPU, memory, storage, networking)
Track record of delivering measurable performance improvements in production systems at scale
Bachelor's degree in computer science or equivalent
Experience with GPU profiling and optimization (CUDA, GPU memory management, inference pipeline tuning)
Familiarity with generative AI infrastructure including model serving, training pipelines, and token economics
Experience with language runtime internals (JVM garbage collection tuning, memory management design, or equivalent)

Nice-to-haves

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Keywords to mirror

airustjava

Platform: amazon
Effort: medium

Benefits

Benefits detected in the listing.

Health Insurance

Dental

Vision

401(K)

Paid Time Off

Parental Leave