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Deep Learning Developer Jobs (NOW HIRING)

Senior Deep Learning Engineer

Redmond, WA · On-site

$62 - $79.75/hr

We are now looking for a Senior Deep Learning Engineer!At NVIDIA, we are at the forefront of advancing the capabilities of artificial intelligence. We are seeking an ambitious and forward-thinking ...

Requirements Candidates for the Deep Learning Algorithm Developer position should have a strong background in engineering, computer science, physics, and/or mathematics. Experience with PyTorch ...

Requirements Candidates for the Deep Learning Algorithm Developer position should have a strong background in engineering, computer science, physics, and/or mathematics. Experience with PyTorch ...

Senior Deep Learning Engineer

$107K - $146K/yr

They are seeking a Senior Deep Learning Engineer to implement core algorithms at the intersection of computer vision and graphics, working with large media datasets to create high-fidelity content.

They are seeking a Deep Learning Engineer to implement core algorithms at the intersection of computer vision and computer graphics, focusing on manipulating large 2D and 3D media datasets.

Required Skills: * Pursuing MS or PhD in Computer Science, Electrical Engineering, Robotics ... Past experiences in deep learning projects involving object detection, motion tracking or semantic ...

Intern, Deep Learning Engineer

Houston, TX · On-site

$14.25 - $19/hr

Train, tune, and optimize deep learning models using our large-scale compute clusters and truck ... Engineering Plus: Experience with Linux, Git, C++, or deployment tools like TensorRT/ONNX.

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Deep Learning Developer information

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How much do deep learning developer jobs pay per hour?

As of Jul 17, 2026, the average hourly pay for deep learning developer in the United States is $38.44, according to ZipRecruiter salary data. Most workers in this role earn between $32.69 and $42.79 per hour, depending on experience, location, and employer.

What is a $900000 AI job?

A $900,000 AI job typically refers to a high-level position in artificial intelligence, such as a senior Deep Learning Developer or AI research lead, often involving advanced skills in machine learning frameworks, data modeling, and programming. Such roles usually require extensive experience, specialized knowledge, and may include responsibilities like developing innovative AI solutions or leading AI teams in tech companies or research institutions.

What are the key skills and qualifications needed to thrive as a Deep Learning Developer, and why are they important?

To thrive as a Deep Learning Developer, you need a strong background in computer science, mathematics, and proficiency in programming languages like Python, often supported by a degree in a related field. Familiarity with deep learning frameworks such as TensorFlow or PyTorch, and experience with cloud platforms or GPU acceleration, are commonly required technical skills. Analytical thinking, problem-solving abilities, and effective teamwork distinguish top performers in this role. These competencies are crucial for designing, training, and deploying advanced neural network models that address complex real-world problems.

What are Deep Learning Developers?

Deep Learning Developers are specialized software engineers or data scientists who design, build, and implement artificial intelligence systems using deep learning techniques. They work with neural networks, large datasets, and various frameworks like TensorFlow or PyTorch to develop models for tasks such as image recognition, natural language processing, and autonomous systems. Their responsibilities include data preprocessing, model training, optimization, and deployment to solve complex problems that require advanced pattern recognition. Deep Learning Developers often collaborate with AI researchers, data engineers, and product teams to integrate intelligent features into applications.

Which 3 jobs will survive AI?

Deep Learning Developers are likely to continue to be in demand as AI advances because they design and improve AI models, requiring specialized skills in programming, mathematics, and data analysis. Other roles expected to persist include AI ethics specialists and AI system trainers, as human oversight and ethical considerations remain essential. These jobs involve complex problem-solving and domain expertise that are difficult to fully automate.

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

AspectDeep Learning DeveloperMachine 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 companies, software firms, industries applying machine learning
Industry UsagePrimarily in AI research, neural network development, deep learning projectsBroader application including predictive modeling, data analysis, and ML systems

Deep Learning Developers specialize in neural networks and deep learning models, often working on AI research and complex algorithms. Machine Learning Engineers have a broader focus on developing, deploying, and maintaining machine learning models across various applications. While both roles require similar educational backgrounds, their focus areas and industry applications differ.

What are some common challenges Deep Learning Developers face when deploying models to production environments?

Deep Learning Developers often encounter challenges such as optimizing model performance for real-time inference, managing resource constraints (like GPU/CPU availability), and ensuring model reproducibility across different environments. Additionally, integrating deep learning models into existing software systems and maintaining them over time can be complex, especially as data and requirements evolve. Collaborating closely with DevOps, data engineers, and QA teams is essential to address these challenges and ensure smooth deployment and ongoing reliability.

What engineer makes $500,000 a year?

Highly experienced deep learning developers or AI engineers with specialized skills in neural networks, large-scale data processing, and advanced machine learning frameworks can earn $500,000 or more annually, especially in senior or leadership roles at major tech companies or startups. Such roles often require advanced degrees, extensive experience, and a strong track record of deploying impactful AI solutions.

What engineers make $300,000 a year?

Deep learning developers and AI engineers with extensive experience, advanced skills in machine learning frameworks, and strong domain expertise can earn $300,000 or more annually, especially in high-demand industries or senior roles. Compensation often includes base salary, bonuses, and stock options, particularly at leading tech companies or startups with significant funding.
What cities are hiring for Deep Learning Developer jobs? Cities with the most Deep Learning Developer job openings:
What states have the most Deep Learning Developer jobs? States with the most job openings for Deep Learning Developer jobs include:
Infographic showing various Deep Learning Developer job openings in the United States as of July 2026, with employment types broken down into 73% Full Time, 25% Part Time, and 2% Contract. Highlights an 72% Physical, 2% Hybrid, and 26% Remote job distribution, with an average salary of $79,957 per year, or $38.4 per hour.
Senior Deep Learning Engineer

Senior Deep Learning Engineer

Nvidia

Redmond, WA • On-site

$62 - $79.75/hr

Full-time

Re-posted 16 days ago


Nvidia rating

9.3

Company rating: 9.3 out of 10

Based on 5 frontline employees who took The Breakroom Quiz

15th of 209 rated software companies


Job description

We are now looking for a Senior Deep Learning Engineer!At NVIDIA, we are at the forefront of advancing the capabilities of artificial intelligence. We are seeking an ambitious and forward-thinking senior deep learning engineer to contribute to the development of next-generation inference optimizations targeting frontier workloads including multi-agent AI systems, generative multimodal models, and inference-time compute scaling. In this role, you will characterize these emerging workloads and develop novel methods to optimize for them across inferencing engines, systems, and hardware architectures. Your work will span multiple tiers of the inference stack from the algorithmic to system level.

As NVIDIA makes significant strides in AI datacenters, our team holds a central role in maximizing the efficiency of our exponentially growing inference deployment needs and establishing a data-driven approach to algorithmic improvements, hardware design and system software development. We collaborate extensively with diverse teams at NVIDIA, spanning deep learning research and framework development teams, to silicon architecture. Thriving in such a high-impact, interdisciplinary environment necessitates not only technical proficiency but also a growth mindset and a pragmatic attitude - qualities that fuel our collective success in shaping the future of datacenter technology.

What You'll Be Doing:

  • Continuously keeping up to date on the latest advancements in generative AI research.

  • Analyzing and prototyping emerging workloads in multi-agent AI systems, generative multimodal models, and inference-time compute scaling.

  • Pioneering and developing optimizations for these workloads across the inference stack to push the boundaries of inferencing quality and speed on NVIDIA systems.

  • Collaborating closely with production teams to incorporate the latest advancements into cutting-edge software frameworks.


What We Need to See:

  • Master's degree (or equivalent experience) in Computer Science, Artificial Intelligence, Applied Mathematics, or related fields.

  • A strong foundation in deep learning, with a particular emphasis on generative models and inferencing.

  • A track record of at least 5 years of relevant software development experience in modern deep learning frameworks such as PyTorch.

  • Growth mindset and pragmatic attitude.

Ways to Stand Out From the Crowd:

  • Published research or noteworthy contributions to the field of deep learning, particularly in areassuch as inference-time compute, multimodal generation, AI systems, etc.

  • Experience with prototyping or deployment of agentic AI systems and/or multimodal generation models.

  • Experience with collaborating across algorithms, software and performance teams to deliver high quality solutions.

  • Familiarity with computer architecture and how it relates to AI algorithms development.


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 on the planet working for us. If you're creative and autonomous, we want to hear from you!

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 - 241,500 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 May 31, 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

Hours and flexibility

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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