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Deep Learning Scientist Jobs (NOW HIRING)

Freenome

Staff Machine Learning Scientist

Brisbane, CA · On-site

$199K - $283K/yr

... with deep learning (DL) methods, a track record of successfully using these methods to answer ... The role reports to the Director, Machine Learning Science. This role can be a Hybrid role based in ...

Natera

OR

This role develops and deploys deep learning models across digital pathology, genomics ... You will collaborate with scientists, pathologists, bioinformaticians, and software engineers to ...

Hayden AI

Deep Learning Engineer II

San Francisco, CA · On-site

$161K - $175K/yr

Deep Learning Engineer II POSITION DUTIES: Lead the research, development, and deployment of ... Master of Science or Engineering degree or the foreign equivalent in Robotics, Machine Learning ...

Freenome

Staff Machine Learning Scientist

Brisbane, CA · On-site +1

$199K - $283K/yr

... with deep learning (DL) methods, a track record of successfully using these methods to answer ... The role reports to the Director, Machine Learning Science. This role can be a Hybrid role based in ...

Freenome

Senior Machine Learning Scientist

Brisbane, CA · On-site +1

$110K - $150K/yr

At Freenome, we are seeking a Senior Machine Learning Scientist to help grow the Machine Learning ... Deep domain-specific experience in computational biology, genomics, proteomics or a related field.

NBCUniversal

Deep Learning Engineer

They are seeking a Deep Learning Engineer to implement core algorithms at the intersection of ... Required : • Graduate degree in Data Science, Computer Science, or a related field (or equivalent ...

Pearson

Lead, Learning Scientist

Hoboken, NJ · Remote

$145K - $165K/yr

Deep learning science expertise (e.g., cognitive load, retrieval, spacing, etc.) * Strong background in assessment design, mastery modelling and diagnostics * Proven experience designing learning ...

Targeted Talent

Senior Deep Learning Engineer

Austin, TX · On-site +1

$130K - $180K/yr

Bachelor's degree in Computer Science, Engineering, or related field * 5+ years of experience, with at least 2 years in both deep learning and software engineering * Proficiency in deep learning ...

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Deep Learning Scientist information

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$37.5K

$122.7K

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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:
What job categories do people searching Deep Learning Scientist jobs look for? The top searched job categories for Deep Learning Scientist jobs are:
Deep Learning Scientist jobs near you
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.
Staff Machine Learning Scientist

Staff Machine Learning Scientist

Freenome

Brisbane, CA • On-site

$199K - $283K/yr

Full-time

Posted 18 days ago

Job description

About this opportunity:
At Freenome, we are seeking a Staff Machine Learning Scientist to help grow the Machine Learning Science team, within the Computational Science department. The ideal candidate has a strong knowledge of artificial intelligence (AI), including machine learning (ML) fundamentals and extensive experience with deep learning (DL) methods, a track record of successfully using these methods to answer complex research questions, the ability to drive independent research and thrive in a highly cross-functional environment.
They will be responsible for the development of algorithms for early, blood-based detection tests for cancer. They will build on a foundation of ML/DL and statistical skills to develop models for identifying molecular signals from blood. They will also work with computational biologists, molecular biologists and ML engineers to design and drive research experiments, and will have a significant impact on the continued growth of an organization dedicated to changing the entire landscape of cancer.
The role reports to the Director, Machine Learning Science. This role can be a Hybrid role based in our Brisbane, California headquarters (2-3 days per week in office), or remote.
What you'll do:
  • Independently pursue cutting edge research in AI applied to biological problems (including cancer research, genomics, computational biology, immunology, etc.).
  • Build new models or fine-tune existing models to identify biological changes resulting from disease.
  • Build models that achieve high accuracy and that generalize robustly to new data.
  • Apply contemporary interpretability techniques to provide a deeper understanding of the underlying signal identified by the model, ideally suggesting potential biological mechanisms.
  • Work closely with ML Engineering partners to ensure that Freenome's computational infrastructure supports optimal model training and iteration.
  • Take a mindful, transparent, and humane approach to your work.

Must haves:
  • PhD or equivalent research experience with an AI emphasis and in a relevant, quantitative field such as Computer Science, Statistics, Mathematics, Engineering, Computational Biology, or Bioinformatics.
  • 6+ years of postdoc or post-PhD industry experience achieving impactful results using relevant modeling techniques.
  • Expertise demonstrated by research publications or industry achievements, in driving independent research in applied machine learning, deep learning and complex data modeling.
  • Practical and theoretical understanding of fundamental ML models like generalized linear models, kernel machines, decision trees and forests, neural networks, boosting and model aggregation.
  • Practical and theoretical understanding of DL models like large language models or other foundation models.
  • Extensive experience with training paradigms like supervised learning, self-supervised learning, and contrastive learning.
  • Proficient in current state of the art in ML/DL approaches in different domains, with an ability to envision their applications in biological data.
  • Proficiency in a general-purpose programming language: Python, R, Java, C, C++, etc.
  • Proficiency in one or more ML frameworks such as; Pytorch, Tensorflow and Jax; and ML platforms like Hugging Face.
  • Experience in ML analysis and developer tools like TensorBoard, MLflow or Weights & Biases.
  • Excellent ability to communicate across disciplines, work collaboratively, and make progress in smaller steps via experimental iterations.
  • Proficient at productive cross-functional scientific communication and collaboration with software engineers and computational biologists.
  • A passion for innovation and demonstrated initiative in tackling new areas of research.

Nice to haves:
  • Deep domain-specific experience in computational biology, genomics, proteomics or a related field.
  • Experience in building DL models for genomic data, with knowledge of state-of-the-art DNA foundation models.
  • Experience in NGS data analysis and bioinformatic pipelines.
  • Experience with containerized cloud computing environments such as Docker in GCP, Azure, or AWS.
  • Experience in a production software engineering environment, including the use of automated regression testing, version control, and deployment systems.

Benefits and additional information:
The US target range of our base salary for new hires is $199,675.00 - $283,500.00. You will also be eligible to receive equity, cash bonuses, and a full range of medical, financial, and other benefits depending on the position offered. Please note that individual total compensation for this position will be determined at the Company's sole discretion and may vary based on several factors, including but not limited to, location, skill level, years and depth of relevant experience, and education. We invite you to check out our career page @ freenome.com/job-openings/ for additional company information.
Freenome is proud to be an equal-opportunity employer, and we value diversity. Freenome does not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, veteran status, or any other status protected under federal, state, or local law.
Applicants have rights under Federal Employment Laws.
  • Family & Medical Leave Act (FMLA)
  • Equal Employment Opportunity (EEO)
  • Employee Polygraph Protection Act (EPPA)

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