Hands-on experience with GPU programming (CUDA, Triton, CUTLASS) and performance optimization. * Proven record of deploying or optimizing deep learning models in production environments. * Experience ...
Hands-on experience with GPU programming (CUDA, Triton, CUTLASS) and performance optimization. * Proven record of deploying or optimizing deep learning models in production environments. * Experience ...
... programming in Python and managing GPUs * Experience using automation to solve problems and improve process efficiency * Experience working with, troubleshooting, tuning, and deploying various types ...
... programming in Python and managing GPUs * Experience using automation to solve problems and improve process efficiency * Experience working with, troubleshooting, tuning, and deploying various types ...
GPU Systems Engineer
New York, NY · On-site
$200K - $300K/yr
... latency programming, FPGA technology, hardware acceleration and machine learning. Our ongoing ... Design, deploy, and scale distributed GPU clusters, from hardware selection and network topology ...
GPU Systems Engineer
New York, NY · On-site
$200K - $300K/yr
... latency programming, FPGA technology, hardware acceleration and machine learning. Our ongoing ... Design, deploy, and scale distributed GPU clusters, from hardware selection and network topology ...
GPU Systems Engineer
New York, NY · Hybrid
$200K - $300K/yr
... latency programming, FPGA technology, hardware acceleration and machine learning. Our ongoing ... Design, deploy, and scale distributed GPU clusters, from hardware selection and network topology ...
GPU Systems Engineer
New York, NY · Hybrid
$200K - $300K/yr
... latency programming, FPGA technology, hardware acceleration and machine learning. Our ongoing ... Design, deploy, and scale distributed GPU clusters, from hardware selection and network topology ...
Senior Deep Learning Software Engineer, Inference
New York, NY · On-site +1
$134K - $176K/yr
GPU programming experience (CUDA, OAI TRITON or CUTLASS) is a plus. Ways to Stand out from The Crowd * Contribute to deep learning software projects, such as PyTorch, vLLM, and SGLang to drive ...
Senior Deep Learning Software Engineer, Inference
New York, NY · On-site +1
$134K - $176K/yr
GPU programming experience (CUDA, OAI TRITON or CUTLASS) is a plus. Ways to Stand out from The Crowd * Contribute to deep learning software projects, such as PyTorch, vLLM, and SGLang to drive ...
Strong knowledge of low-level GPU programming with CUDA, including Tensor Cores, cooperative groups, graphs, and warp-level intrinsics * Expertise in internals of deep-learning frameworks like ...
Strong knowledge of low-level GPU programming with CUDA, including Tensor Cores, cooperative groups, graphs, and warp-level intrinsics * Expertise in internals of deep-learning frameworks like ...
... GPU programming and libraries (e.g., Pytorch, JAX, CUDA, XLA, Triton, or PTX). • Demonstrated ability to quickly solve complex computational problems, create inspiring technical demos, and ...
... GPU programming and libraries (e.g., Pytorch, JAX, CUDA, XLA, Triton, or PTX). • Demonstrated ability to quickly solve complex computational problems, create inspiring technical demos, and ...
... programming in Python and managing GPUs * Experience using automation to solve problems and improve process efficiency * Experience working with, troubleshooting, tuning, and deploying various types ...
... programming in Python and managing GPUs * Experience using automation to solve problems and improve process efficiency * Experience working with, troubleshooting, tuning, and deploying various types ...
Machine Learning Research Engineer (MLRE) - GPUs
New York, NY · On-site +1
$164K - $259K/yr
Deep understanding of GPU programming fundamentals. * Solid track record of observable artifacts (e.g., GitHub) showing optimization work. * Experience collaborating on software projects across multi ...
Machine Learning Research Engineer (MLRE) - GPUs
New York, NY · On-site +1
$164K - $259K/yr
Deep understanding of GPU programming fundamentals. * Solid track record of observable artifacts (e.g., GitHub) showing optimization work. * Experience collaborating on software projects across multi ...
Software Engineer, GPU Fleet
New York, NY · On-site
$200K - $300K/yr
... latency programming, FPGA technology, hardware acceleration and machine learning. Our ongoing ... Summary: Our GPU fleet is one of the fastest-growing and most critical parts of the firm ...
Software Engineer, GPU Fleet
New York, NY · On-site
$200K - $300K/yr
... latency programming, FPGA technology, hardware acceleration and machine learning. Our ongoing ... Summary: Our GPU fleet is one of the fastest-growing and most critical parts of the firm ...
Software Engineer, GPU Fleet
$200K - $300K/yr
... latency programming, FPGA technology, hardware acceleration and machine learning. Our ongoing ... Summary: Our GPU fleet is one of the fastest-growing and most critical parts of the firm ...
Software Engineer, GPU Fleet
$200K - $300K/yr
... latency programming, FPGA technology, hardware acceleration and machine learning. Our ongoing ... Summary: Our GPU fleet is one of the fastest-growing and most critical parts of the firm ...
Software Engineer - GPU Fabric Observability
Manhattan, NY · On-site
$160 - $260/hr
Join us and help build the platform engineers turn to to ship AI products. THE ROLE Baseten is building its own GPU infrastructure for large-scale inference. As we move into large scale, high-density ...
Software Engineer - GPU Fabric Observability
Manhattan, NY · On-site
$160 - $260/hr
Join us and help build the platform engineers turn to to ship AI products. THE ROLE Baseten is building its own GPU infrastructure for large-scale inference. As we move into large scale, high-density ...
Forward Deployed Engineer - ML
New York, NY · On-site
$180K - $250K/yr
... GPU programming, or ML infrastructure * Familiarity with the serving (e.g., vLLM, SGLang) and training (e.g., slime, verl, TRL) toolchains. You don't need all of these, but you should be able to go ...
Forward Deployed Engineer - ML
New York, NY · On-site
$180K - $250K/yr
... GPU programming, or ML infrastructure * Familiarity with the serving (e.g., vLLM, SGLang) and training (e.g., slime, verl, TRL) toolchains. You don't need all of these, but you should be able to go ...
Senior Inference Engineer, GPU Kernel Optimization
$134K - $176K/yr
We're now looking for a Sr. Inference Engineer, for GPU Kernel Optimization! What does it take to push every LLM inference operation to its performance ceiling? Our LLM Inference Performance Analysis ...
Senior Inference Engineer, GPU Kernel Optimization
$134K - $176K/yr
We're now looking for a Sr. Inference Engineer, for GPU Kernel Optimization! What does it take to push every LLM inference operation to its performance ceiling? Our LLM Inference Performance Analysis ...
Staff Observability Platform Engineer (AI / GPU Infrastructure)
Manhattan, NY · On-site
$118K - $155K/yr
Strong programming skills in Go and/or n Python . * Solid understanding of production engineering ... Preferred Qualifications * Experience with GPU infrastructure , AI/ML platforms , or HPC ...
Staff Observability Platform Engineer (AI / GPU Infrastructure)
Manhattan, NY · On-site
$118K - $155K/yr
Strong programming skills in Go and/or n Python . * Solid understanding of production engineering ... Preferred Qualifications * Experience with GPU infrastructure , AI/ML platforms , or HPC ...
Machine Learning Researcher
Manhattan, NY · On-site
... GPU programming and libraries (e.g., Pytorch, JAX, CUDA, XLA, Triton, or PTX). • Demonstrated ability to quickly solve complex computational problems, create inspiring technical demos, and ...
Machine Learning Researcher
Manhattan, NY · On-site
... GPU programming and libraries (e.g., Pytorch, JAX, CUDA, XLA, Triton, or PTX). • Demonstrated ability to quickly solve complex computational problems, create inspiring technical demos, and ...
Senior Applied Research Scientist - GPU Native Numerical Algorithms
New York, NY · On-site +1
$107K - $137K/yr
We are looking for an Applied Research Scientist to join our computational engineering applied research team! In this role, we will work together to design GPU-native numerical methods that make ...
Senior Applied Research Scientist - GPU Native Numerical Algorithms
New York, NY · On-site +1
$107K - $137K/yr
We are looking for an Applied Research Scientist to join our computational engineering applied research team! In this role, we will work together to design GPU-native numerical methods that make ...
The Quality Engineering team is looking for an experienced GPU ASIC and PCBA Debug and Failure Analysis Engineering Manager to lead and develop a team of FA engineers. This role is intended for a ...
The Quality Engineering team is looking for an experienced GPU ASIC and PCBA Debug and Failure Analysis Engineering Manager to lead and develop a team of FA engineers. This role is intended for a ...
Failure Analysis Engineering Manager, GPU ASIC and PCBA Debug
Secaucus, NJ · On-site
$120 - $150/hr
THE ROLE The Quality Engineering team is looking for an experienced GPU ASIC and PCBA Debug and Failure Analysis Engineering Manager to lead and develop a team of FA engineers. This role is intended ...
Failure Analysis Engineering Manager, GPU ASIC and PCBA Debug
Secaucus, NJ · On-site
$120 - $150/hr
THE ROLE The Quality Engineering team is looking for an experienced GPU ASIC and PCBA Debug and Failure Analysis Engineering Manager to lead and develop a team of FA engineers. This role is intended ...
The Quality Engineering team is looking for an experienced GPU ASIC and PCBA Debug and Failure Analysis Engineering Manager to lead and develop a team of FA engineers. This role is intended for a ...
The Quality Engineering team is looking for an experienced GPU ASIC and PCBA Debug and Failure Analysis Engineering Manager to lead and develop a team of FA engineers. This role is intended for a ...
Gpu Programming information
See New York salary details
$36.1K - $42.3K
5% of jobs
$42.3K - $48.5K
10% of jobs
$48.5K - $54.8K
7% of jobs
$55.9K is the 25th percentile. Wages below this are outliers.
$54.8K - $61K
15% of jobs
$61K - $67.2K
7% of jobs
The median wage is $69.4K / yr.
$67.2K - $73.4K
15% of jobs
$73.4K - $79.6K
11% of jobs
$84.3K is the 75th percentile. Wages above this are outliers.
$79.6K - $85.8K
6% of jobs
$85.8K - $92K
14% of jobs
$92K - $98.3K
7% of jobs
$98.3K - $104.5K
2% of jobs
$36.1K
$71.1K
$104.5K
How much do gpu programming jobs pay per year?
As of Aug 26, 2026, the average yearly pay for gpu programming in New York is $71,084.00, according to ZipRecruiter salary data. Most workers in this role earn between $55,200.00 and $87,500.00 per year, depending on experience, location, and employer.
What is GPU programming?
A GPU Programming job involves writing and optimizing code to run on Graphics Processing Units (GPUs) for parallel computing tasks. This role is commonly found in fields like machine learning, scientific computing, gaming, and data analytics. GPU programmers use languages such as CUDA, OpenCL, or Vulkan to accelerate computations and improve performance. They work closely with software engineers and data scientists to optimize algorithms for high-performance applications.
What types of projects or applications do GPU programmers commonly work on?
GPU Programmers are often involved in developing or optimizing software for high-performance applications such as machine learning, scientific simulations, real-time rendering in gaming and visualization, and video/image processing tools. Their daily work may include collaborating with software engineers, data scientists, and hardware teams to create efficient, scalable parallel algorithms that leverage GPU capabilities. The role frequently requires problem-solving to maximize computational efficiency and troubleshooting complex performance bottlenecks. By working across multidisciplinary teams, GPU Programmers help deliver robust solutions for data-intensive problems in areas like healthcare, finance, automotive technology, and entertainment.
What are the key skills and qualifications needed to thrive in GPU programming, and why are they important?
To excel in GPU Programming, you need a strong background in parallel computing concepts, mathematics, and proficiency in languages such as CUDA, OpenCL, or DirectX/OpenGL, often supported by a degree in computer science, engineering, or a related field. Familiarity with NVIDIA and AMD GPU development tools, performance profilers, and possibly certifications like NVIDIA's Deep Learning Institute courses are valuable. Teamwork, effective communication, and strong problem-solving abilities are essential soft skills in this field. These competencies enable efficient development, optimization, and integration of high-performance GPU code in real-world applications.
What are popular job titles related to Gpu Programming jobs in New York?
For Gpu Programming jobs in New York, the most frequently searched job titles are:
- From Home Caribbean Islands Engineer
- Transmission Engineer Remote
- Remote Time Attendance
- Remote Contract Game Developer
- Remote Ai Mathematics Trainer
- Remote Wire Harness Designer
- Remote Junior Computer Engineer
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- Remote Powerbuilder Developer Day Shift
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What job categories do people searching Gpu Programming jobs in New York look for?
The top searched job categories for Gpu Programming jobs in New York are:
Engineering Manager, Deep Learning Inference
New York, NY • On-site, Remote
Nvidia •
Computer and Electronic Product Manufacturing • 10K+ employees10,000+ employees
9.6
Based on 18 frontline employees who took The Breakroom Quiz
7th of 246 rated software companies
Great coworkers
People enjoy working here
Good employer
Respectful managers
Learn new skills
Full-time
Posted 21 days ago
Job description
NVIDIA is seeking an exceptional Manager, Deep Learning Inference Software, to lead a world-class engineering team advancing the state of AI model deployment. You will shape the software powering today's most sophisticated AI systems - from large language models to multimodal generative AI - all accelerated on NVIDIA GPUs. The Deep Learning Inference team develops and optimizes open-source frameworks that make AI deployment scalable, efficient, and accessible - including vLLM / SGLang, and FlashInfer. Our work enables developers worldwide to harness NVIDIA accelerators for real-time inference at every scale, from datacenter clusters to edge devices.
What you'll be doing:
Lead, mentor, and scale a high-performing engineering team focused on deep learning inference and GPU-accelerated software.
Drive the strategy, roadmap, and execution of NVIDIA's inference frameworks engineering, focusing on Client AI.
Partner with internal compiler, libraries, and research teams to deliver end-to-end optimized inference pipelines across NVIDIA accelerators.
Oversee performance tuning, profiling, and optimization of large-scale models for LLM, multimodal, and generative AI applications.
Guide engineers in adopting best practices for CUDA, Triton, CUTLASS, and multi-GPU communications (NIXL, NCCL, NVSHMEM).
Represent the team in roadmap and planning discussions, ensuring alignment with NVIDIA's broader AI and software strategies.
Foster a culture of technical excellence, open collaboration, and continuous innovation.
What we need to see:
MS, PhD, or equivalent experience in Computer Science, Electrical/Computer Engineering, or a related field.
6+ overall years of software development experience, including 3+ years in technical leadership or engineering management.
Strong background in C/C++ software design and development; proficiency in Python is a plus.
Hands-on experience with GPU programming (CUDA, Triton, CUTLASS) and performance optimization.
Proven record of deploying or optimizing deep learning models in production environments.
Experience leading teams using Agile or collaborative software development practices.
Ways to Stand out from The Crowd:
Significant open-source contributions to deep learning or inference frameworks such as PyTorch, vLLM / SGLang, Triton, or TensorRT-LLM.
Deep understanding of multi-GPU communications (NIXL, NCCL, NVSHMEM) and distributed inference architectures.
Expertise in performance modeling, profiling, and system-level optimization across CPU and GPU platforms.
Proven ability to mentor engineers, guide architectural decisions, and deliver complex projects with measurable impact.
Publications, patents, or talks on LLM serving, model optimization, or GPU performance engineering.
With highly competitive salaries and a comprehensive benefits package, 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 and, our rapid growth means endless opportunities for career advancement. If you're a passionate technical leader ready to shape the future of AI inference frameworks - and build the software that powers the world's most advanced models - we'd love to hear from you.
#LI-Hybrid
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 2, and 224,000 USD - 356,500 USD for Level 3.You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive 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.
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
