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Deep Learning Performance Architect Jobs (NOW HIRING)

Tenstorrent

Performance Architect, AI HW

$170K/yr

... deep learning workloads into architectural insight and measurable design tradeoffs. • Curious ... in high-performance AI systems. • Benchmark and analyze complex AI workloads across single and ...

AT&T

Lead Performance Architect

Atlanta, GA · On-site

$94K - $141K/yr

Performance Architects focus on performance outcomes-not just training-by evaluating root causes ... Collaborate with Learning Design and Delivery teams to develop and deploy performance solutions.

Nvidia

Senior Performance Architect, Nemotron

Redmond, WA · On-site

$187K/yr

We are now looking for a Senior Performance Architect for Nemotron! At NVIDIA, we are redefining ... Experience with deep learning frameworks like PyTorch, TRT-LLM, VLLM, SGLang * A Growth mindset and ...

AT and T

Lead Performance Architect

Atlanta, GA · On-site

$94K - $141K/yr

Performance Architects focus on performance outcomes-not just training-by evaluating root causes ... Collaborate with Learning Design and Delivery teams to develop and deploy performance solutions.

Nvidia

Senior Performance Architect, Nemotron

Redmond, WA · On-site

$187K/yr

We are now looking for a Senior Performance Architect for Nemotron. At NVIDIA, we are redefining ... Experience with deep learning frameworks like PyTorch, TRT-LLM, VLLM, SGLang A Growth mindset and ...

Nvidia

Senior Performance Architect, Nemotron

Hillsboro, OR · On-site

$181K/yr

We are now looking for a Senior Performance Architect for Nemotron! At NVIDIA, we are redefining ... Experience with deep learning frameworks like PyTorch, TRT-LLM, VLLM, SGLang * A Growth mindset and ...

Nvidia

Senior Performance Architect, Nemotron

Hillsboro, OR · On-site

$181K/yr

We are now looking for a Senior Performance Architect for Nemotron. At NVIDIA, we are redefining ... Experience with deep learning frameworks like PyTorch, TRT-LLM, VLLM, SGLang A Growth mindset and ...

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Deep Learning Performance Architect information

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$156.5K

$168K

How much do deep learning performance architect jobs pay per year?

As of Sep 2, 2026, the average yearly pay for deep learning performance architect in the United States is $167,842.00, according to ZipRecruiter salary data. Most workers in this role earn between $167,000.00 and $167,000.00 per year, depending on experience, location, and employer.

What is a deep learning performance architect?

A Deep Learning Performance Architect is a specialized professional who designs, analyzes, and optimizes the performance of deep learning systems and models. They work to improve the efficiency, speed, and scalability of machine learning algorithms on various hardware platforms such as GPUs, TPUs, and CPUs. Their role often involves collaborating with software engineers and data scientists to identify bottlenecks and implement solutions that enhance computational capabilities for AI workloads. By doing so, they ensure that deep learning applications run faster and more efficiently, making the best use of available resources.

What are the key skills and qualifications needed to thrive as a deep learning performance architect?

To thrive as a Deep Learning Performance Architect, you need a strong background in computer science, deep learning frameworks, parallel computing, and optimization techniques, typically supported by a relevant degree and experience in AI or high-performance computing. Familiarity with tools such as TensorFlow, PyTorch, CUDA, and profiling or benchmarking systems is essential. Analytical problem-solving, effective communication, and a collaborative mindset help professionals excel in cross-functional teams and resolve complex performance bottlenecks. These skills are vital for optimizing AI workloads, ensuring scalability, and maximizing the efficiency of deep learning models in production environments.

What are some common challenges faced by deep learning performance architects when optimizing large-scale neural network models?

Deep Learning Performance Architects often encounter challenges such as balancing model accuracy with computational efficiency, managing memory constraints on specialized hardware, and optimizing inference or training speed across different platforms. They frequently need to profile and analyze bottlenecks at both the algorithmic and hardware levels, often requiring close collaboration with software engineers and hardware designers. Staying current with rapidly evolving deep learning frameworks and hardware accelerators is also essential to ensure optimal performance and scalability.

What is the difference between Deep Learning Performance Architect vs Machine Learning Engineer?

AspectDeep Learning Performance ArchitectMachine Learning Engineer
CredentialsAdvanced degrees in AI, deep learning, or related fields; certifications in deep learning frameworksDegrees in computer science, data science, or related fields; certifications in machine learning tools
Work EnvironmentResearch labs, AI development teams, performance optimization settingsData-driven projects, model development, deployment environments
Industry UsageTech companies, AI research firms, organizations focusing on deep learning optimizationTech companies, startups, enterprises applying machine learning solutions

The Deep Learning Performance Architect specializes in optimizing deep learning models for efficiency and scalability, focusing on hardware and software performance. In contrast, Machine Learning Engineers develop, train, and deploy machine learning models across various applications. While both roles require strong technical skills, the Architect emphasizes performance tuning and system optimization, whereas the Engineer focuses on model development and implementation.

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What job categories do people searching Deep Learning Performance Architect jobs look for?

The top searched job categories for Deep Learning Performance Architect jobs are:

Deep Learning Performance Architect jobs near you
Infographic showing various Deep Learning Performance Architect job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 87% Full Time, 10% Part Time, and 2% Contract. Highlights an 86% Physical, 2% Hybrid, and 12% Remote job distribution, with an average salary of $167,842 per year, or $80.7 per hour.

Senior Deep Learning Kernel Software Performance Architect

Nvidia

Santa Clara, CA • On-site

$152K - $206K/yr

Full-time

Re-posted 22 days ago

Nvidia rating

9.6

Company rating: 9.6 out of 10

Based on 18 frontline employees who took The Breakroom Quiz

6th of 247 rated software companies

Job description

We are now looking for a Senior Kernel Performance Architect for Deep Learning Software. NVIDIA is seeking extraordinary architects to develop processor and system architectures that accelerate machine learning, data analytics and high-performance computing applications. This position offers the chance to create a meaningful impact in a dynamic, technology-focused company.

What you will be doing: Craft GPU-accelerated system architectures that push the boundaries of deep learning performance. Prototype high-performance software for deep learning and data analytics workloads. Analyze, visualize, and optimize software performance using analytical models, simulators, and test suites.

Collaborate closely across NVIDIA teams such as: CUDA Compiler teams to identify performance issues. AI/ML training and inference performance teams to identify and optimize critical deep learning layers. hardware architecture performance teams to define expectation for emerging deep learning hardware features.

What we need to see: A Master's or PhD in Computer Science, Electrical Engineering or Computer Engineering, or equivalent experience. 5+ years of relevant industry or research experience. A strong foundation in machine learning and deep learning fundamentals to complement your expertise in computer architecture.

A strong background in high performance kernel (such as CUTLASS), work experience on math library performance analysis and profiling to identify performance bottlenecks. Fluency in programming languages such as Python, C, C++. Experience and familiarity with GPU computing and parallel programming models.

You have firsthand work experience with analytical performance modeling, profiling, and analysis. NVIDIA is widely considered to be one of the technology world's most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us.

Are you creative architect interested in pushing silicon to its highest performance. If so, we want to hear from you. Come, join our DL Architecture team and help build the real-time, cost-effective AI computing platform driving our success in this exciting and quickly growing field.

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 152,000 USD - 218,500 USD for Level 3, and 184,000 USD - 287,500 USD for Level 4. You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until January 17, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

What Nvidia employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom

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About Nvidia

Sourced by ZipRecruiter

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It's a unique legacy of innovation that's fueled by great technology--and amazing people. Today, we're tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what's never been done before takes vision, innovation, and the world's best talent.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Santa Clara, CA, US

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