ZipRecruiter

Log In

1

Deep Learning Scientist Jobs (NOW HIRING)

Spotter

Machine Learning Scientist

Culver City, CA · On-site

For more information about Spotter, please visit Overview We're looking for a talented and intensely curious Machine Learning Scientist with deep expertise in building and deploying production ...

Carbon Robotics

Deep Learning Engineer

Seattle, WA · On-site

As a Deep Learning Engineer, you will design, develop, and deploy deep learning systems for ... Computer Science, Machine Learning, or a related field (or equivalent experience) • We're a ...

Carbon Robotics

Deep Learning Engineer

Seattle, WA

Experience mentoring engineers and contributing to team technical culture Requirements * 2-7 years of experience in deep learning model optimization and deployment * BS+ in Computer Science, Machine ...

Matroid

Deep Learning Engineer

Palo Alto, CA · On-site

$170K - $300K/yr

Masters in Computer Science, Software Engineering, Mathematics, or equivalent * Passion for computer vision and deep learning; you are excited to adapt the latest multimodal LLMs, or implement a ...

Carbon Robotics

Deep Learning Engineer

Seattle, WA · On-site

$14/hr

Experience mentoring engineers and contributing to team technical culture Requirements * 2-7 years of experience in deep learning model optimization and deployment * BS+ in Computer Science, Machine ...

Expedia

Senior Machine Learning Scientist

San Jose, CA · Remote

$107K - $146K/yr

Senior Machine Learning Scientist The Senior Machine Learning Scientist is responsible for building ... Applies deep expertise in applied ML, Generative AI, and rigorous experimentation to design robust ...

Expedia

Senior Machine Learning Scientist

Seattle, WA · Remote

$104K - $142K/yr

Senior Machine Learning Scientist The Senior Machine Learning Scientist is responsible for building ... Applies deep expertise in applied ML, Generative AI, and rigorous experimentation to design robust ...

next page

Showing results 1-20

All JobsDeep Learning Scientist Jobs

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 Jul 3, 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.

Will MLE be replaced by AI?

As a Deep Learning Scientist, machine learning engineering (MLE) involves designing and deploying models, which AI advancements can automate or enhance. However, MLE roles require expertise in data handling, model optimization, and domain knowledge that AI tools support but do not fully replace. Human oversight remains essential for ensuring model accuracy, ethical considerations, and system integration.

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.

Which 3 jobs will survive AI?

Deep Learning Scientists are likely to continue to be in demand as AI advances, especially in research, model development, and complex problem-solving roles. Jobs that require high levels of creativity, emotional intelligence, or physical dexterity, such as healthcare professionals, skilled trades, and creative artists, are also expected to persist. Combining technical skills with domain expertise will enhance job security in an AI-driven future.

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 Scientist or AI executive, with compensation including salary, bonuses, and stock options. These roles often require advanced expertise in machine learning, deep learning frameworks, and extensive industry experience, and they are usually found in leading tech companies or AI-focused organizations.

Is ML a high paying job?

Machine Learning (ML) roles, including positions like Deep Learning Scientist, are generally well-paid due to the specialized skills required, such as programming in Python, experience with neural networks, and knowledge of frameworks like TensorFlow or PyTorch. Salaries vary based on experience, location, and industry, but these roles tend to offer above-average compensation compared to many other tech jobs.
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 June 2026, with employment types broken down into 100% Full Time. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $122,738 per year, or $59 per hour.
Machine Learning Scientist

Machine Learning Scientist

Spotter

Culver City, CA • On-site

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 13 days ago

Job description

Overview
Spotter empowers the world's best Creators with capital, data, and insights to scale their programming into sustainable media businesses. Through these partnerships, Spotter helps brands partner with creator-led franchises to unlock growth, amplify impact, and build lasting cultural relevance.
Spotter has already deployed over $980 million to Creators to reinvest in themselves and accelerate their growth, with plans to reach $1 billion in investment in 2026. With a premium catalog that spans over 725,000 videos, Spotter generates more than 88 billion monthly watch-time minutes, delivering a unique scaled media solution to Advertisers and Ad Agencies that is transparent, efficient, and 100% brand safe. For more information about Spotter, please visit https://spotter.com.
Overview
We're looking for a talented and intensely curious Machine Learning Scientist with deep expertise in building and deploying production machine learning models, particularly in areas such as deep learning, reinforcement learning, contextual bandits, ranking, personalization, recommendation systems, and adaptive learning systems. You thrive in a fast-paced startup environment and are motivated by building models that don't just perform well in experiments, they ship to production and create real value for YouTube creators.
In this role, you'll train, evaluate, optimize, and deploy a wide range of machine learning models, from neural networks and ranking systems to contextual bandits, recommendation models, sequential decision-making systems, and traditional machine learning approaches. You're passionate about staying at the forefront of AI and machine learning, especially in areas where models learn from feedback, adapt over time, and improve real-world product outcomes.
We're a team of builders who value continuous learning, rapid experimentation, and delivering AI solutions that make a measurable difference for creators. If you enjoy solving complex problems, iterating quickly, and building intelligent products that help the world's top YouTube creators work smarter and create better content, you'll thrive at Spotter.
What You'll Do
You'll develop machine learning models that move beyond experimentation and into production, where they directly improve creator workflows and product experiences. Working alongside Analytics, Product, and Engineering, you'll help develop intelligent systems that improve how creators discover insights, make decisions, and create content.
Your work may include:
  • Designing, training, evaluating, optimizing, and deploying production machine learning models.
  • Building recommendation, ranking, and personalization systems that adapt to creator behavior, product feedback, and changing objectives.
  • Applying reinforcement learning, contextual bandits, online learning, and other adaptive learning approaches where they improve product outcomes.
  • Designing systems that balance exploration and exploitation, short-term performance and long-term value, and multiple competing product objectives.
  • Developing reward models, feedback models, and objective functions that translate noisy, sparse, delayed, or implicit signals into reliable model training and evaluation targets.
  • Working with logged interaction data to understand user behavior, evaluate model performance, improve decision quality, and reduce bias in model evaluation.
  • Applying offline policy evaluation, counterfactual evaluation, causal inference, or related techniques to reason about model changes before and after deployment.
  • Designing experiments to evaluate model performance, measure product impact, and continuously improve production systems.
  • Building scalable model training, evaluation, deployment, and inference pipelines.
  • Optimizing models for accuracy, latency, scalability, reliability, and production maintainability.
  • Working with structured and unstructured datasets using Python and SQL.
  • Collaborating closely with Product and Engineering to translate customer problems into machine learning solutions.
  • Staying current with advances in reinforcement learning, recommendation systems, ranking, personalization, deep learning, experimentation, and production ML, and thoughtfully applying new techniques where they create measurable value.

Who You Are
  • Master's degree or PhD in Computer Science, Statistics, Applied Mathematics, Electrical Engineering, Physics, or another quantitative field.
  • 5+ years building, evaluating, and deploying machine learning models in production environments.
  • Strong experience with modern deep learning frameworks and production ML workflows.
  • Experience building one or more of the following:
    • recommendation systems
    • ranking systems
    • personalization models
    • reinforcement learning systems
    • contextual bandits
    • online learning systems
    • adaptive decision-making systems
  • Strong understanding of reinforcement learning concepts such as exploration vs. exploitation, reward design, policy evaluation, delayed feedback, feedback loops, and sequential decision-making.
  • Experience working with logged interaction data, behavioral data, or feedback signals to train, evaluate, and improve models.
  • Experience designing experiments and using data to improve model performance in real-world product environments.
  • Experience with offline evaluation, A/B testing, counterfactual reasoning, causal inference, or other methods for measuring model impact.
  • Experience training, evaluating, tuning, and deploying machine learning models across deep learning and traditional ML approaches.
  • Strong understanding of embeddings, representation learning, neural networks, sequence modeling, and modern deep learning architectures.
  • Strong Python and SQL skills.
  • Excellent communication skills and the ability to work cross-functionally with Product, Engineering, Analytics, and other stakeholders.
  • Curiosity, ownership, and a passion for building products that customers love.

Nice to Have
  • Experience with large-scale recommendation, ranking, personalization, or adaptive optimization systems.
  • Familiarity with ad recommendation, ad ranking, or campaign optimization systems used by large-scale platforms, such as YouTube, Google, Meta, TikTok, Amazon, or similar consumer marketplace platforms.
  • Experience serving large-scale ML models in production.
  • Experience building machine learning systems for large-scale digital platforms, such as creator platforms, consumer apps, recommendation systems, ad recommendation systems, campaign optimization systems, or workflow automation tools.

Why Spotter
  • Medical insurance covered up to 100%
  • Dental & vision insurance
  • 401(k) matching
  • Stock options
  • Discretionary PTO
  • Complimentary gym access
  • Autonomy and upward mobility
  • Diverse, equitable, and inclusive culture, where your voice matters.

In compliance with local law, we are disclosing the compensation, or a range thereof, for roles that will be performed in Culver City. Actual salaries will vary and may be above or below the range based on various factors including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. A reasonable estimate of the current pay range is: $167K-$185K salary per year. The range listed is just one component of Spotter's total compensation package for employees. Other rewards may include an annual discretionary bonus and equity.
Spotter is an equal opportunity employer. Spotter does not discriminate in employment on the basis of race, religion, creed, color, national origin, ancestry, citizenship, physical or mental disability, medical condition, genetic characteristics or information, marital status, sex (including pregnancy, childbirth, breastfeeding, and related medical conditions), gender, gender identity, gender expression, age, sexual orientation, military status, veteran status, use of or request for family or medical leave, political affiliation, or any other status protected under applicable federal, state or local laws.
Equal access to programs, services and employment is available to all persons. Those applicants requiring reasonable accommodations as part of the application and/or interview process should notify a representative of the Human Resources Department.

Apply