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.
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.
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 https://amazon.jobs/content/en/how-we-hire/accommodations 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. As a total compensation company, Amazon's package may include other elements such as sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon offers comprehensive benefits including health insurance (medical, dental, vision, prescription, basic life & AD&D insurance), Registered Retirement Savings Plan (RRSP), Deferred Profit Sharing Plan (DPSP), paid time off, and other resources to improve health and well-being. We thank all applicants for their interest, however only those interviewed will be advised as to hiring status.
CAN, BC, Vancouver - 150,700.00 - 251,700.00 CAD annually
CAN, ON, Toronto - 150,700.00 - 251,700.00 CAD annually
