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Deep Learning Scientist Jobs (NOW HIRING)

Carbon Robotics

Deep Learning Engineer

Seattle, WA · On-site

... Science, Machine Learning, or a related field (or equivalent experience) Preferred : • Experience in autonomous driving or ADAS is a plus -- background in perception pipelines, sensor fusion, or ...

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 ...

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 · On-site

$140K - $220K/yr

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 ...

Occulytics Inc.

Deep Learning Engineer

They are seeking a Deep Learning Engineer to build and deploy cutting-edge deep learning models ... data scientists, product managers, and domain experts to understand business requirements ...

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 ...

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 ...

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Deep Learning Scientist information

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$37.5K

$122.7K

$196.5K

How much do deep learning scientist jobs pay per year?

As of Jul 25, 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.
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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 July 2026, with employment types broken down into 70% Full Time, 28% Part Time, and 2% Contract. Highlights an 68% Physical, 3% Hybrid, and 29% Remote job distribution, with an average salary of $122,738 per year, or $59 per hour.
Senior Machine Learning Scientist (Sensor Intelligence)

Senior Machine Learning Scientist (Sensor Intelligence)

Whoop

Boston, MA

$150K - $215K/yr

Other

Posted 4 days ago

Job description

At WHOOP, we're on a mission to unlock human performance and healthspan. WHOOP empowers members to perform at a higher level through a deeper understanding of their bodies and daily lives.

WHOOP is seeking a Senior Machine Learning Scientist to join the Sensor Intelligence Group (SIG), a cross-functional team collaborating across WHOOP Labs, Firmware, and Machine Learning and Research. This role focuses on developing compact machine learning models for edge deployment and is central to scaling AI systems that power WHOOP's most foundational health features. In this role, you'll develop next-generation, personalized AI from prototyping to productization, ultimately delivering personalized coaching to millions of WHOOP members.

RESPONSIBILITIES:

  • Research, prototype, and productize lightweight deep learning models suitable for resource-constrained edge targets

  • Drive deep learning model customization and compression strategies such as distillation, pruning, fine-tuning, and quantization-aware training

  • Collaborate with product teams to define member experience targets and with cloud-focused machine learning teams to implement distributed AI systems

  • Lead build/buy decisions by evaluating commercial and open-source model performance for WHOOP use cases

  • Stay current in Edge AI industry trends and best practices and mentor junior team members

QUALIFICATIONS:

  • Bachelor's degree in Computer Science, Electrical/Computer Engineering, Applied Mathematics, or a related field; Master's or PhD degree preferred

  • 5+ years of experience as a Machine Learning Scientist or similar role with a focus on advanced development, preferably related to voice and/or text-based conversational systems 

  • Demonstrated experience training, fine-tuning, and deploying state-of-the-art deep learning architectures to resource-constrained embedded targets

  • Experience pre-training and fine-tuning small language models and/or building natural language understanding (NLU) models than run on resource-constrained targets

  • Experience with cloud platforms (AWS or GCP) and familiarity with modern MLOps practices such as CI/CD, model versioning, monitoring, and observability

  • Strong communication and collaboration skills across cross-functional teams

  • Strong commitment to embracing and leveraging AI tools in day-to-day tasks, ensuring AI-assisted work aligns with the same high-quality standards as personal contributions

ADDITIONAL DESIRABLE EXPERIENCE:

  • Experience deploying deep learning models to microcontrollers or other resource-constrained edge devices using toolchains such as TFLite/LiteRT or ExecuTorch, and with inference libraries such as CMSIS-NN or CMSIS-DSP

  • Experience developing machine learning models for consumer-facing products

  • Experience building multi-modal datasets, including speech, video, text, or physiological signals, for human-AI interaction

  • Familiarity with time-series foundation models and self-supervised learning methods

This role is based in the WHOOP office located in Boston, MA. The successful candidate must be prepared to relocate if necessary to work out of the Boston, MA office.
 
Interested in the role, but don't meet every qualification? We encourage you to still apply! At WHOOP, we believe there is much more to a candidate than what is written on paper, and we value character as much as experience. As we continue to build a diverse and inclusive environment, we encourage anyone who is interested in this role to apply.
 
WHOOP is an Equal Opportunity Employer and participates in E-verify to determine employment eligibility.  It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.
 
The WHOOP compensation philosophy is designed to attract, motivate, and retain exceptional talent by offering competitive base salaries, meaningful equity, and consistent pay practices that reflect our mission and core values.
 
At WHOOP, we view total compensation as the combination of base salary, equity, and benefits, with equity serving as a key differentiator that aligns our employees with the long-term success of the company and allows every member of our corporate team to own part of WHOOP and share in the company's long-term growth and success.
 
The U.S. base salary range for this full-time position is $150,000 - $215,000 Salary ranges are determined by role, level, and location. Within each range, individual pay is based on factors such as job-related skills, experience, performance, and relevant education or training. 
 
In addition to the base salary, the successful candidate will also receive benefits and a generous equity package.
 
These ranges may be modified in the future to reflect evolving market conditions and organizational needs. While most offers will typically fall toward the starting point of the range, total compensation will depend on the candidate's specific qualifications, expertise, and alignment with the role's requirements.
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About Whoop

Sourced by ZipRecruiter

At WHOOP, we're on a mission to unlock human performance. WHOOP empowers users (Olympians, Professional Athletes, Fitness Enthusiasts, etc) to perform at a higher level through a deeper understanding of their bodies and daily lives.

Industry

Fitness and sports centers

Company size

501 - 1,000 Employees

Headquarters location

Boston, MA, US

Year founded

2012

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