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Deep Learning Ai Jobs (NOW HIRING)

Toyon

Deep Learning Algorithm Developer

Goleta, CA

$120K - $200K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Our researchers apply AI/ML techniques to develop data processing automation solutions for problems ... Requirements Candidates for the Deep Learning Algorithm Developer position should have a strong ...

Toyon

Deep Learning Algorithm Developer

Fort Collins, CO

$100K - $190K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Our researchers apply AI/ML techniques to develop data processing automation and control solutions ... Requirements Candidates for the Deep Learning Algorithm Developer position should have a strong ...

Anson McCade

AI Researcher / AI Engineer

Miami, FL · On-site

$250K/yr

Strong experience in Machine Learning, Deep Learning, Large Language Models, AI Infrastructure, or Distributed Systems * Excellent programming skills in Python and/or C++ * Experience developing and ...

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How much do deep learning ai jobs pay per year?

As of Aug 16, 2026, the average yearly pay for deep learning ai in the United States is $83,885.00, according to ZipRecruiter salary data. Most workers in this role earn between $72,000.00 and $139,000.00 per year, depending on experience, location, and employer.

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

AspectDeep Learning AiMachine Learning Engineer
Required CredentialsDegree in Computer Science, Data Science, or related fields; knowledge of neural networksDegree in Computer Science, Data Science, or related fields; programming skills in Python, R
Work EnvironmentResearch labs, AI development teams, tech companies focusing on AI modelsSoftware development teams, data analysis projects across various industries
Industry UsagePrimarily in AI research, autonomous systems, NLP, computer visionAcross industries for predictive modeling, data analysis, automation

Deep Learning Ai specialists focus on designing and implementing neural network models for complex AI tasks, often requiring advanced knowledge of deep neural networks. Machine Learning Engineers develop broader machine learning models, including traditional algorithms. While both roles require similar educational backgrounds, Deep Learning Ai roles are more specialized in neural networks and AI research, whereas Machine Learning Engineers work across a wider range of algorithms and applications.

What are some common challenges faced by professionals working in deep learning AI, and how can they be addressed?

Professionals in Deep Learning AI often encounter challenges such as managing large datasets, ensuring model accuracy, and addressing issues like overfitting. Collaboration with data engineers and domain experts is crucial to ensure high-quality data and relevant feature selection. Additionally, staying up-to-date with rapidly evolving frameworks and algorithms requires continuous learning and participation in knowledge-sharing within the team. Regular code reviews and experimentation with different architectures can help overcome technical obstacles and improve model performance.

What are the key skills and qualifications needed to thrive as a deep learning AI engineer?

To thrive as a Deep Learning AI Engineer, you need a strong background in mathematics, programming (especially Python), and experience with neural networks, typically supported by a degree in computer science, engineering, or a related field. Proficiency with deep learning frameworks such as TensorFlow or PyTorch, and knowledge of tools like CUDA for GPU acceleration, are essential; relevant certifications can be advantageous. Analytical thinking, creativity, and effective communication are important soft skills for solving complex problems and collaborating with cross-functional teams. These skills and qualities are crucial for building robust AI models and driving innovation in this rapidly evolving field.

What is a deep learning AI professional?

Deep Learning AI professionals are experts who design, develop, and implement artificial intelligence systems that use deep neural networks to analyze complex data and solve tasks such as image recognition, natural language processing, and autonomous decision-making. They work with large datasets and advanced algorithms to build models that can learn and improve over time. These professionals often have a background in computer science, mathematics, or engineering, and are skilled in programming languages like Python and frameworks such as TensorFlow or PyTorch.
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Deep Learning Ai jobs near you
Infographic showing various Deep Learning Ai 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 $83,885 per year, or $40.3 per hour.

Senior Compiler Engineer, AI Inference Platforms

NVIDIA

Santa Clara, CA • On-site

$122K - $168K/yr

Full-time

Re-posted 23 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

Job Summary:
NVIDIA is known as 'the AI computing company' and is seeking a Senior Compiler Engineer for its Deep Learning & AI Compiler team. The role involves analyzing deep learning networks and developing compiler optimization algorithms to enhance NVIDIA's inference engine across various platforms.
Responsibilities:
• Analyzing deep learning networks and developing compiler optimization algorithms.
• Collaborating with members of the deep learning software framework teams and the GPU architecture teams to accelerate the next generation of deep learning software.
• Scope of these efforts includes defining public APIs, performance optimizations and analysis, crafting and implementing compiler techniques for AI workloads and future NVIDIA GPUs.
Qualifications:
Required:
• Bachelor’s, Master’s or Ph.D. in Computer Science, Computer Engineering, related field or equivalent experience.
• 3+ years of relevant work or research experience in performance analysis and compiler optimizations.
• Experience with compiler technologies (e.g., MLIR, LLVM, XLA, Triton, etc.).
• Excellent C/C++ and Python programming and software design skills, including debugging, performance analysis, and test design.
• Ability to work independently, define project goals and scope, and lead your own development efforts.
• Strong interpersonal skills are required along with the ability to work in a dynamic product-oriented team.
Preferred:
• Proficient in CPU and/or GPU architecture.
• CUDA or OpenCL programming experience.
• Understanding of deep learning models, algorithms and frameworks, such as PyTorch, JAX.
• GPU kernel authoring and performance analysis using tools such as Nsight Compute.
• A track record of success in mentoring early-career engineers and interns is a bonus.
• Track record on new hardware bring-up is a plus.
Company:
NVIDIA is a computing platform company operating at the intersection of graphics, HPC, and AI. Founded in 1993, the company is headquartered in Santa Clara, USA, with a team of 10001+ employees. The company is currently Late Stage.

What Nvidia employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom

Nvidia logo

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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