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Deep Learning Software Engineer Jobs (NOW HIRING)

Carbon Robotics

Deep Learning Engineer

Seattle, WA · On-site

As a Deep Learning Engineer, you will design, develop, and deploy deep learning systems for ... software stack, including sensor integration and web services, ideally within a robotics or ...

Carbon Robotics

Deep Learning Engineer

Seattle, WA · On-site

As a Deep Learning Engineer, you will design, develop, and deploy deep learning systems for ... software stack, including sensor integration and web services, ideally within a robotics or ...

Carbon Robotics

Deep Learning Engineer

Seattle, WA · On-site

$140K - $220K/yr

YouTube | X | Instagram | LinkedIn | News Deep Learning Engineer As a Deep Learning Engineer at ... Hands-on experience working across the software stack, including sensor integration and web ...

Carbon Robotics

Deep Learning Engineer

Seattle, WA · On-site

As a Deep Learning Engineer, you will design, develop, and deploy deep learning systems for ... software stack, including sensor integration and web services, ideally within a robotics or ...

Carbon Robotics

Deep Learning Engineer

Seattle, WA · On-site

$140K - $220K/yr

  • Medical

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YouTube | X | Instagram | LinkedIn | News Deep Learning Engineer As a Deep Learning Engineer at ... Hands-on experience working across the software stack, including sensor integration and web ...

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Deep Learning Software Engineer information

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$63.5K

$147.5K

$205.5K

How much do deep learning software engineer jobs pay per year?

As of Aug 13, 2026, the average yearly pay for deep learning software engineer in the United States is $147,524.00, according to ZipRecruiter salary data. Most workers in this role earn between $120,000.00 and $173,000.00 per year, depending on experience, location, and employer.

What is the difference between Deep Learning Software Engineer vs Machine Learning Engineer?

AspectDeep Learning Software EngineerMachine Learning Engineer
Required CredentialsBachelor's or Master's in CS, AI, or related; experience with neural networksBachelor's or Master's in CS, Data Science, or related; knowledge of algorithms
Work EnvironmentResearch labs, AI startups, tech companies focusing on neural networksData-driven teams across various industries, including finance, healthcare, and tech
Industry UsagePrimarily in AI research, autonomous systems, NLP, computer visionBroader applications including predictive modeling, recommendation systems

Deep Learning Software Engineers specialize in neural networks and AI models, often working on complex AI systems. Machine Learning Engineers have a broader focus on developing and deploying machine learning models across various domains. While overlapping in skills, their roles differ in focus and application areas.

More about Deep Learning Software Engineer jobs
What job categories do people searching Deep Learning Software Engineer jobs look for? The top searched job categories for Deep Learning Software Engineer jobs are:
Deep Learning Software Engineer jobs near you
Infographic showing various Deep Learning Software Engineer job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 23% Part Time, 1% Temporary, and 2% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $147,524 per year, or $70.9 per hour.

Senior Software Engineer, CUDA Deep Learning Systems

Nvidia Corporation

Santa Clara, CA • On-site

$143K - $189K/yr

Full-time

Re-posted 28 days ago

Nvidia rating

9.6

Company rating: 9.6 out of 10

Based on 17 frontline employees who took The Breakroom Quiz

7th of 244 rated software companies

Job description

We are looking for an experienced and highly motivated software professional to work on pioneering initiatives and projects at the intersection of CUDA and Deep Learning Systems. As the complexity and scale of artificial intelligence continue to grow, the intersection of advanced deep learning architectures, massive-scale distributed computing, and low-level hardware optimization has never been more critical. Our team is dedicated to exploring and prototyping next-generation ideas that bridge the gap between deep learning algorithms and CUDA, pushing the boundaries of what is possible on modern accelerator architectures.
Join our dynamic, research-oriented team to help unlock maximum hardware performance for emerging AI workloads. You will be a crucial member of a highly technical group exploring uncharted territories in model optimization, custom kernel development, and cluster-scale AI systems design. If you are passionate about the fundamentals of deep learning and thrive on squeezing every ounce of performance out of advanced computing systems from a single GPU to supercomputer clusters, we want you on our team!
What you will be doing:
  • Explore, research, and prototype novel systems optimizations for advanced deep learning models at the intersection of high-level DL frameworks and low-level CUDA through modeling, simulation, and silicon prototyping.
  • Architect and optimize distributed computing systems that scale seamlessly from a single node to massive, cluster-scale supercomputing environments.
  • Design, implement, and optimize custom high-performance CUDA kernels tailored to emerging neural network architectures and workloads.
  • Analyze complex hardware-software interactions to identify and resolve performance bottlenecks in both training and inference pipelines.
  • Collaborate closely with AI researchers, HW and SW architects, kernel and compiler authors and CUDA driver experts to co-design systems and algorithms that improve accelerator compute utilization, memory bandwidth, cross-node network communication efficiency and programmability.
  • Develop exploratory tools and runtime systems to profile and accelerate new paradigms in deep learning.
  • Write clean, effective, and maintainable code, ensuring exploratory prototypes can smoothly transition into open-source releases, upstream framework integrations, internal tools, or closed-source commercial products.

What we need to see:
  • BS, MS, or PhD degree in Computer Science, Computer Engineering, Electrical Engineering, or related field (or equivalent experience).
  • 8+ years of relevant industry experience or equivalent academic experience after degree achievement.
  • Strong proficiency in C++ and Python programming.
  • Solid background in the fundamentals of Deep Learning with a focus on transformers.
  • Strong understanding of distributed computing principles, multi-node scaling, and the unique performance challenges of cluster-scale execution.
  • Proven experience in systems programming, computer architecture, and low-level systems performance optimization.
  • Familiarity with deep learning accelerator architectures such as the GPU and hands-on experience with CUDA programming and kernel optimization.
  • A strong analytical approach with experience using profiling tools to deeply understand software performance on hardware.
  • Experience profiling and optimizing innovative vision models, generative AI architectures, or diffusion models.
  • Background in deep learning compilers, both graph-level and codegen (e.g., Triton, XLA, torch compile)

Ways to stand out from the crowd:
  • Deep expertise in the performance internals and execution graphs of major deep learning autograd, training and inference frameworks (e.g., PyTorch, JAX, TensorRT, vLLM, sgLang, Nemo, Megatron, MaxText, etc.).
  • Hands-on experience with CUDA, communication libraries (e.g., NCCL, MPI, UCX) and distributed machine learning techniques (e.g., pipeline parallelism, tensor parallelism).
  • Knowledge of numerical methods, low-precision arithmetic (e.g., NVFP4, MXFP4, FP8, INT8), and their implications on deep learning model accuracy and performance.
  • Familiarity with systems requirements for Reinforcement Learning (RL) or highly parallel simulation environments and/or research background in machine learning systems or adjacent fields.
  • Experience with machine learning, especially agentic systems, applied to systems problems.

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 4, and 224,000 USD - 356,500 USD for Level 5.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until August 7, 2026.
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.

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

Year founded

1993

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