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Principal Performance Engineer Jobs (NOW HIRING)

ARM

Principal AI Performance Engineer

San Jose, CA · Hybrid

In collaboration with multiple teams across Arm's engineering organization, you will diagnose and resolve performance challenges, and use these insights to influence Arms IP and tooling roadmaps.

Nvidia

OR · On-site

We are looking for a Principal Performance and Manufacturing Architect who has built the models ... You have applied AI to production engineering workflows - model fitting, anomaly detection, and ...

Leonardo DRS

Senior Performance Engineer

Fitchburg, MA · On-site

$102K - $159K/yr

... Senior Principal. * Strong experience in turbomachinery, steam turbines, gas turbines, rotating ... performance tradeoffs in turbine systems. * Experience using commercial CFD and engineering ...

Leonardo DRS

Senior Performance Engineer

Fitchburg, MA

$102K - $159K/yr

... Senior Principal. * Strong experience in turbomachinery, steam turbines, gas turbines, rotating ... performance tradeoffs in turbine systems. * Experience using commercial CFD and engineering ...

Nvidia

We are looking for a Principal Performance and Manufacturing Architect who has built the models ... You have applied AI to production engineering workflows - model fitting, anomaly detection, and ...

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How much do principal performance engineer jobs pay per year?

As of Jul 7, 2026, the average yearly pay for principal performance engineer in the United States is $147,220.00, according to ZipRecruiter salary data. Most workers in this role earn between $118,500.00 and $173,000.00 per year, depending on experience, location, and employer.

What is a Principal Performance Engineer?

A Principal Performance Engineer is a senior technical expert responsible for ensuring that software systems and applications operate at optimal performance levels. They lead efforts in designing, analyzing, and improving system architectures to prevent performance bottlenecks and scalability issues. Their work often includes conducting performance testing, identifying system limitations, and collaborating with development teams to implement solutions. Principal Performance Engineers also mentor other engineers and set best practices for performance optimization across the organization.

How does a Principal Performance Engineer typically collaborate with development and operations teams to optimize application performance?

A Principal Performance Engineer plays a pivotal role in bridging development and operations by working closely with both teams to identify, analyze, and resolve performance bottlenecks. They often participate in design reviews, consult on architectural decisions, and guide the implementation of best practices for scalability and reliability. Regularly, they lead performance testing efforts, share insights from monitoring tools, and mentor team members on performance tuning. This collaborative approach ensures that performance considerations are integrated throughout the software development lifecycle, resulting in robust and efficient applications.

What is the difference between Principal Performance Engineer vs Performance Engineer?

AspectPrincipal Performance EngineerPerformance Engineer
CredentialsBachelor's/Master's in Computer Science or related, often with certifications in performance testingBachelor's in Computer Science or related, with similar certifications
Work EnvironmentLeads performance testing teams, designs strategies, and mentorsExecutes performance tests, analyzes results, and reports findings
Industry UsageUsed across large enterprises, tech companies, and software firmsCommon in software development, QA teams, and IT departments

The Principal Performance Engineer typically holds a leadership role, overseeing performance testing strategies and mentoring teams, while the Performance Engineer focuses on executing tests and analyzing results. Both roles require similar technical skills and certifications, but differ in scope and responsibility.

What are the key skills and qualifications needed to thrive as a Principal Performance Engineer, and why are they important?

To thrive as a Principal Performance Engineer, you need deep expertise in system architecture, performance analysis, and optimization, typically supported by a degree in computer science or a related field. Mastery of performance profiling tools (like JMeter, LoadRunner, or Dynatrace), scripting languages, and cloud platforms is essential, along with relevant industry certifications. Strong problem-solving, communication, and leadership skills help you collaborate across teams and drive system improvements. These skills ensure scalable, efficient, and reliable systems that meet business and user requirements in complex technical environments.
More about Principal Performance Engineer jobs
What job categories do people searching Principal Performance Engineer jobs look for? The top searched job categories for Principal Performance Engineer jobs are:
Principal Performance Engineer jobs near you
Principal AI Performance Engineer

Principal AI Performance Engineer

Advanced Micro Devices, Inc

San Jose, CA • On-site

$210K/yr

Full-time

Posted 28 days ago

Advanced Micro Devices rating

8.4

Company rating: 8.4 out of 10

Based on 7 frontline employees who took The Breakroom Quiz

22nd of 141 rated electronics manufacturers

Job description

WHAT YOU DO AT AMD CHANGES EVERYTHING
At AMD, our mission is to build great products that accelerate next-generation computing experiences-from AI and data centers, to PCs, gaming and embedded systems. Grounded in a culture of innovation and collaboration, we believe real progress comes from bold ideas, human ingenuity and a shared passion to create something extraordinary. When you join AMD, you'll discover the real differentiator is our culture. We push the limits of innovation to solve the world's most important challenges-striving for execution excellence, while being direct, humble, collaborative, and inclusive of diverse perspectives. Join us as we shape the future of AI and beyond. Together, we advance your career.
THE ROLE:
AMD is looking for a performance-obsessed engineer to drive AI inference performance to the absolute limit on AMD GPUs. You will lead a small, highly technical team and work end-to-end across the stack: profiling, diagnosing, and optimizing leading models on customer-relevant serving configurations (e.g. agentic coding, long-context, high-throughput serving). You move from challenge to challenge, tackling the hardest performance problems across our most strategic customer engagements and leaving behind measurable uplifts and reusable methodology. This is not a sustaining role: every engagement is different, every optimization leaves a lasting impact.
THE PERSON:
You can take any AI workload, understand it top to bottom, and make it faster. You are equally comfortable profiling a distributed serving deployment, diagnosing a kernel-level bottleneck, and presenting optimization results to a customer's VP of Engineering. You understand GPU kernel performance deeply: not just how to use profiling tools, but how to reason about occupancy, cache behavior, memory coalescing, and instruction-level bottlenecks from first principles. You lead through technical depth: you set the standard for your team by doing the hardest work yourself and pulling others up along the way. You are AI-fluent, not just in the models you optimize, but in how you work: you leverage AI agents and tools daily to accelerate your workflows, and you actively define new ways of using them to make yourself and your team more effective. You thrive under pressure, move fast, and measure everything.
KEY RESPONSIBILITIES:
  • Drive performance optimization end-to-end across the stack on leading models and customer-relevant serving configurations, closing competitive gaps through kernel and systems-level optimizations
  • Profile, diagnose, and resolve the hardest cross-stack performance bottlenecks, from GPU kernels and operator dispatch to framework-level scheduling and multi-node communication
  • Diagnose kernel-level performance issues using profiling tools: identify occupancy limitations, L2 cache thrashing, register pressure, memory coalescing issues, etc, and translate findings into actionable optimizations
  • Lead customer-facing technical engagements: present findings, recommend optimizations, and deliver measurable performance uplifts
  • Integrate and optimize custom kernels (Triton, Gluon, CK, PyDSL, ASM, AITER) within serving frameworks, understanding dispatch paths, shape extraction, and backend selection
  • Optimize multi-node distributed inference: communication-compute overlap, parallelism strategies, and scale-out performance
  • Develop and refine shared performance optimization methodology that raises the bar across the broader team
  • Leverage AI agents to accelerate daily work and define best practices for AI-assisted performance engineering
  • Upstream optimizations into open-source frameworks such as vLLM, SGLang, and PyTorch

PREFERRED EXPERIENCE:
  • 7+ years of software development experience in GPU computing, AI systems, or high-performance computing
  • Deep hands-on experience with AI serving frameworks (vLLM, SGLang, TensorRT-LLM, or similar) and their internals
  • Strong background in end-to-end workload profiling and bottleneck diagnosis: you can trace from user request to GPU kernel and back
  • Understanding of GPU kernel performance characteristics: occupancy, register and LDS pressure, memory coalescing, cache utilization, wavefront scheduling, and instruction-level bottlenecks
  • Ability to read and reason about kernel-level profiling data and translate it into concrete optimization actions. You may not write kernels from scratch daily, but you can tell exactly why one is slow and what needs to change
  • Understanding of model architectures (transformers, MoE, diffusion), inference paradigms (speculative decoding, prefill-decode disaggregation, continuous batching), and how they map to hardware
  • Experience with custom kernel development or integration (HIP, CUDA, Triton, CK, or similar)
  • Understanding of multi-GPU and multi-node distributed systems: scale-up and scale-out topologies, RCCL/NCCL, RDMA, and communication-compute overlap
  • System and rack-level design awareness: understanding performance tradeoffs across the full deployment stack
  • Strong proficiency in Python and C++
  • Customer-facing technical leadership experience: ability to engage with customers, present findings, and drive decisions
  • Fluent in AI-assisted development: daily user of AI agents and tools, with a mindset toward defining new AI-powered workflows
  • Strong Linux systems knowledge
  • Excellent written and verbal English communication skills

ACADEMIC CREDENTIALS:
Bachelor's, Master's, or PhD in Computer Science, Computer Engineering, Electrical Engineering, or equivalent. Advanced degree preferred but exceptional industry experience valued equally.
LOCATION:
San Jose, CA, preferred
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Benefits offered are described: AMD benefits at a glance.
AMD 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.
AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD's "Responsible AI Policy" is available here.
This posting is for an existing vacancy.

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