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Remote Deep Learning Engineer Jobs (NOW HIRING)

Senior Machine Learning Engineer Remote US & Canada We are seeking a highly motivated and ... Have deep passion for video games and think on behalf of our players * Be curious, a problem solver ...

Senior/Staff Machine Learning Engineer

$107K - $146K/yr

By combining advanced machine learning, probabilistic modeling, and deep geoscience expertise ... We are looking for a talented deep learning engineer or scientist to lead the development of this ...

Have expert understanding of a range of ML techniques, such as deep learning, optimization ... Whether you're a Remote employee or work from the Office, we want you to start your journey here by ...

Senior Machine Learning Engineer

Boston, MA · On-site +1

$133K - $175K/yr

Strong understanding of various machine learning algorithms,Large Language Models, and deep ... Flexible working arrangements (remote or hybrid options available). * The opportunity to work on ...

Senior Machine Learning Engineer

Boston, MA · Remote

$125K - $165K/yr

Strong understanding of various machine learning algorithms,Large Language Models, and deep ... Flexible working arrangements (remote or hybrid options available). * The opportunity to work on ...

Senior Machine Learning Engineer

Boston, MA · On-site +1

$133K - $175K/yr

Strong understanding of various machine learning algorithms,Large Language Models, and deep ... Flexible working arrangements (remote or hybrid options available). * The opportunity to work on ...

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Remote Deep Learning Engineer information

See salary details

$11K

$83.9K

$140K

How much do remote deep learning engineer jobs pay per year?

As of Aug 10, 2026, the average yearly pay for remote deep learning engineer 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.

How do remote deep learning engineers typically collaborate with cross-functional teams despite working remotely?

Remote Deep Learning Engineers frequently collaborate with data scientists, product managers, and software engineers using digital tools such as Slack, Zoom, and collaborative code platforms like GitHub. Regular virtual meetings and sprint planning sessions help ensure alignment on project goals and milestones. Clear documentation and asynchronous communication are crucial for effective teamwork, especially when team members are in different time zones. This collaborative structure enables remote engineers to contribute meaningfully to model development, deployment, and integration while maintaining flexibility.

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

To thrive as a Remote Deep Learning Engineer, you need a strong background in machine learning, deep learning frameworks, and programming languages like Python, usually supported by a degree in computer science or a related field. Familiarity with tools such as TensorFlow, PyTorch, cloud platforms (e.g., AWS, GCP), and version control systems is typically required, with certifications in AI or cloud technologies being advantageous. Excellent problem-solving, communication, and self-management skills make candidates stand out in remote environments. These skills and qualities are essential for developing effective AI solutions, collaborating across distributed teams, and driving innovation in the fast-evolving field of deep learning.

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

AspectRemote Deep Learning EngineerRemote Machine Learning Engineer
Required CredentialsBachelor's/Master's in CS, AI, or related; experience with deep learning frameworksBachelor's/Master's in CS, Data Science, or related; experience with ML algorithms
Work EnvironmentResearch and development, model training, neural network designData analysis, model deployment, algorithm development
Employer & Industry UsageTech companies, AI startups, research institutionsTech firms, finance, healthcare, e-commerce

Remote Deep Learning Engineers focus on designing and training neural networks for complex AI tasks, while Remote Machine Learning Engineers work on broader ML models and algorithms. Both roles require strong programming skills and knowledge of machine learning frameworks, but Deep Learning Engineers specialize in neural networks and large-scale data processing.

What is a remote deep learning engineer?

A Remote Deep Learning Engineer is a professional who works primarily online to design, develop, and implement deep learning models and algorithms. These engineers use neural networks and large datasets to solve complex problems in fields like computer vision, natural language processing, and more. Working remotely, they collaborate with team members via digital tools, write code, optimize models, and often deploy solutions to cloud environments. This role requires strong programming skills, experience with deep learning frameworks (like TensorFlow or PyTorch), and the ability to work independently in a distributed team setting.
More about Remote Deep Learning Engineer jobs
What cities are hiring for Remote Deep Learning Engineer jobs? Cities with the most Remote Deep Learning Engineer job openings:
What are the most commonly searched types of Deep Learning Engineer jobs? The most popular types of Deep Learning Engineer jobs are:
What states have the most Remote Deep Learning Engineer jobs? States with the most job openings for Remote Deep Learning Engineer jobs include:
Infographic showing various Remote Deep Learning Engineer 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.

Machine Learning Engineer

Harvard University

Boston, MA • On-site, Remote

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted 13 days ago


Harvard University rating

8.5

Company rating: 8.5 out of 10

Based on 12 frontline employees who took The Breakroom Quiz

80th of 617 rated colleges and universities


Job description

Company Description

By working at Harvard University, you join a vibrant community that advances Harvard's world-changing mission in meaningful ways, inspires innovation and collaboration, and builds skills and expertise. We are dedicated to creating a diverse and welcoming environment where everyone can thrive.

Why join Harvard Medical School?

Harvard Medical School's mission is to nurture a diverse, inclusive community dedicated to alleviating suffering and improving health and well-being for all through excellence in teaching and learning, discovery and scholarship, and service and leadership.

You'll be at the heart of biomedical discovery, education, and innovation, working alongside world-renowned faculty and a community dedicated to improving human health. This is more than a job - it's an opportunity to shape the future of medicine.

Job Description

The Core for Computational Biomedicine (CCB) in the Department of Biomedical Informatics (DBMI) at Harvard Medical School (HMS) is looking for a Machine Learning Engineer with advanced expertise to lead development of large language models (LLMs) to advance CCB's mission to leverage data and computation to transform research and education, and to improve health outcomes. CCB provides computational and analytic resources to advance scientific discovery within HMS through its multi-disciplinary team of computational and quantitative scientists who work on collaborative projects both within the center and with members of the HMS community. The selected candidate will play a pivotal role in advancing the center's mission to harness the power of computational techniques in the field of medicine. By developing medical LLMs, the engineer will contribute to educating the next generation of medical students and enhancing clinical decision-making processes.
Key Responsibilities:

  • Develop, implement, and optimize medical large language models tailored to the needs of medical education and clinical decision support.
  • Collaborate with interdisciplinary teams comprising biologists, clinicians, and data scientists to understand domain-specific requirements and translate them into computational solutions.
  • Stay updated with the latest advancements in deep learning and machine learning to ensure the models developed are state-of-the-art.
  • Develop infrastructures for data transformation and ingestion.
  • Build AI models that make predictions based on large quantities of data.
  • Explain the usefulness of the AI models created to stakeholders.
  • Transform machine learning models into APIs to interact with other applications.
  • Use expert knowledge to lead research AI and data science projects. 
Qualifications

Basic Qualifications:

  • Minimum of seven years' post-secondary education or relevant work experience.


Additional Qualifications and Skills:

  • A Master's or PhD in Computer Science, Computational Biology, or a related field is strongly preferred.
  • Minimum of 3 years of hands-on experience in developing complex deep learning solutions to tackle scientific challenges.
  • Proficiency with the Python deep learning software stack, particularly expertise in PyTorch, Numpy, and related packages.
  • Experience handling and processing large and diverse datasets, especially medical texts, journals, or electronic health records.
  • Ability to collaborate effectively with non-technical stakeholders, such as doctors and medical researchers.
  • Experience with experiment tracking and project management tools, notably frameworks like Weights & Biases.
  • Prior experience in fine-tuning large language models for specific tasks.
  • Demonstrated experience in optimizing deep learning models for better performance and efficiency.
  • Understanding of biology and/or medicine to bridge the gap between pure machine learning and its applications in the medical field.
  • A track record of publications in technical conferences or journals.
Additional Information
  • Standard Hours/Schedule: 35 hours per week
  • Visa Sponsorship Information: Harvard University is unable to provide visa sponsorship for this position.
  • Pre-Employment Screening: Identity, Education, Criminal
  • Other Information: Please note that we are currently conducting a majority of interviews and onboarding remotely and virtually. We appreciate your understanding.
  • Staying Informed About Your Application: Due to the high volume of applications, we may not always be able to reach out right away, but you can track your status anytime through the Careers@Harvard portal. 

#LI-DK1

Work Format Details

This position has been determined by school or unit leaders that some of the duties and responsibilities can be effectively performed at a non-Harvard location. The work schedule and location will be set by the department at its discretion and based upon operational needs. When not working at a Harvard or Harvard-designated location, employees in hybrid positions must work in a Harvard registered state in compliance with the University's Policy on Employment Outside of Massachusetts. Additional details will be discussed during the interview process. Certain visa types and funding sources may limit work location. Individuals must meet work location sponsorship requirements prior to employment.

Salary Grade and Ranges

This position is salary grade level 060. Please visit Harvard's Salary Ranges to view the corresponding salary range and related information. 

Benefits

Harvard offers a comprehensive benefits package that is designed to support a healthy work-life balance and your physical, mental and financial wellbeing. Because here, you are what matters. Our benefits include, but are not limited to: 

  • Generous paid time off including parental leave 
  • Medical, dental, and vision health insurance coverage starting on day one 
  • Retirement plans with university contributions 
  • Wellbeing and mental health resources 
  • Support for families and caregivers 
  • Professional development opportunities including tuition assistance and reimbursement 
  • Commuter benefits, discounts and campus perks 

Learn more about these and additional benefits on our Benefits & Wellbeing Page. 

EEO/Non-Discrimination Commitment Statement

Harvard University is committed to equal opportunity and non-discrimination. We seek talent from all parts of society and the world, and we strive to ensure everyone at Harvard thrives. Our differences help our community advance Harvard's academic purposes.

Harvard has an equal employment opportunity policy that outlines our commitment to prohibiting discrimination on the basis of race, ethnicity, color, national origin, sex, sexual orientation, gender identity, veteran status, religion, disability, or any other characteristic protected by law or identified in the university's non-discrimination policy. Harvard's equal employment opportunity policy and non-discrimination policy help all community members participate fully in work and campus life free from harassment and discrimination.


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