1

Deep Learning Scientist Jobs (NOW HIRING)

Involves utilizing various techniques, such as random forests, deep learning, and neural networks ... Engage with the broader scientific community through publications, talks, and open-source; Keep up ...

Involves utilizing various techniques, such as random forests, deep learning, and neural networks ... Engage with the broader scientific community through publications, talks, and open-source; * Keep ...

$123K - $185K/yr

Involves utilizing various techniques, such as random forests, deep learning, and neural networks ... Engage with the broader scientific community through publications, talks, and open-source; Keep up ...

Deep Learning Intern

Santa Clara, CA · On-site

$19 - $65/hr

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 ...

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 ...

Deep Learning Researcher

New York, NY · On-site

$200K - $300K/yr

PhD degree in computer science or a related field * Mastery of deep learning techniques and proven ... track record of building sophisticated predictive models using deep learning * Publications in ...

next page

Showing results 1-20

Deep Learning Scientist information

See salary details

$37.5K

$122.7K

$196.5K

How much do deep learning scientist jobs pay per year?

As of Jun 7, 2026, the average yearly pay for deep learning scientist in the United States is $122,738.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,500.00 and $136,000.00 per year, depending on experience, location, and employer.

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

To thrive as a Deep Learning Scientist, you need a solid background in machine learning, statistics, and programming, often supported by an advanced degree in computer science or a related field. Familiarity with deep learning frameworks like TensorFlow or PyTorch, experience with cloud computing platforms, and proficiency in Python are typically required. Strong problem-solving skills, creativity, and the ability to communicate complex ideas clearly set outstanding candidates apart. These capabilities are essential for developing innovative AI solutions, interpreting results, and collaborating effectively in multidisciplinary teams.

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

AspectDeep Learning ScientistMachine Learning Engineer
Required CredentialsMaster's or PhD in Computer Science, Data Science, or related fields; strong background in deep learning frameworksBachelor's or Master's in Computer Science or related fields; proficiency in machine learning algorithms and software engineering
Work EnvironmentResearch-focused, experimental, often in R&D teamsDevelopment and deployment-focused, working on production systems
Employer & Industry UsageTech companies, research labs, AI startupsTech firms, finance, healthcare, and industries deploying ML models

While both roles involve machine learning, Deep Learning Scientists focus on developing advanced neural network models and research, whereas Machine Learning Engineers implement, optimize, and deploy these models in real-world applications.

What are Deep Learning Scientists?

Deep Learning Scientists are experts who design, develop, and implement advanced machine learning models inspired by the structure and function of the brain, known as artificial neural networks. They work with large datasets to train algorithms that can recognize patterns, make predictions, and solve complex problems in areas such as image recognition, natural language processing, and autonomous systems. Deep Learning Scientists often collaborate with software engineers, data scientists, and domain specialists to deploy models in real-world applications like healthcare, finance, and self-driving cars.

What are some typical challenges faced when working as a Deep Learning Scientist, and how can they be addressed?

Deep Learning Scientists often encounter challenges such as managing large datasets, tuning complex model architectures, and ensuring reproducibility of experiments. Handling these issues requires strong skills in data preprocessing, familiarity with version control systems, and experience with frameworks like TensorFlow or PyTorch. Collaborating closely with cross-functional teams—including data engineers, software developers, and domain experts—can also help in overcoming technical and project-related obstacles. Continuous learning and staying updated with the latest research is essential to excel in this rapidly evolving field.
More about Deep Learning Scientist jobs
What cities are hiring for Deep Learning Scientist jobs? Cities with the most Deep Learning Scientist job openings:
What states have the most Deep Learning Scientist jobs? States with the most job openings for Deep Learning Scientist jobs include:
Infographic showing various Deep Learning Scientist job openings in the United States as of May 2026, with employment types broken down into 16% Full Time, 77% Part Time, and 7% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $122,738 per year, or $59 per hour.

Deep Learning Scientist - eFinancialCareers

eFinancialCareers

Manhattan, NY

Full-time

Posted 19 days ago


Job description

Are you ready to be at the forefront of deep learning research? Our growth-stage company, fresh from securing significant investment, is on the lookout for an exceptionally talented Deep Learning Scientist. This is a rare chance to join one of the most innovative AI labs in the industry, where machine learning and deep learning are not just part of our strategy but the heart of our success.

What We Offer:

  1. A Cutting-Edge Environment: Dive into a workplace that embodies the cutting edge of AI research, offering unparalleled freedom and resources to explore and implement DL & ML technologies.
  2. Autonomy and Influence: You will have the autonomy to shape the direction of our AI initiatives, deciding 'how and when to apply DL & ML research' to drive our business forward.
  3. Collaboration with Industry Leaders: Work alongside some of the world's most respected professionals in AI, in an environment that thrives on collaboration and the entrepreneurial spirit.
  4. Impactful Work: Your work will not only push the boundaries of AI but also have a tangible impact across various units within our business, thanks to your engagement with key stakeholders.

Who We Are Seeking:

  1. An Expert in Deep Learning Research: Demonstrates a consistent history of outstanding research, including experience with transition models and sequence-to-sequence architecture; proficiency in reinforcement learning and deep neural network concepts is essential to bring value.
  2. A Champion of Collaboration: Exudes enthusiasm for cooperative work, eager to exchange knowledge and grow alongside some of the sector's brightest.