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How much do engineering fellow jobs pay per year?

As of Sep 14, 2026, the average yearly pay for engineering fellow in the United States is $59,022.00, according to ZipRecruiter salary data. Most workers in this role earn between $49,000.00 and $66,500.00 per year, depending on experience, location, and employer.

What is the difference between Engineering Fellow vs Senior Engineer?

AspectEngineering FellowSenior Engineer
CredentialsAdvanced degrees, extensive experience, industry recognitionTypically a bachelor's or master's degree with several years of experience
Work EnvironmentResearch, strategic projects, thought leadership rolesProject execution, design, development within teams
Employer & Industry UsageResearch institutions, tech companies, industry leadersMost engineering firms, corporate R&D, manufacturing

The main difference between an Engineering Fellow and a Senior Engineer lies in their level of expertise and role focus. Engineering Fellows are recognized for their industry leadership and often work on strategic or innovative projects, while Senior Engineers focus on project execution and technical development. Both roles require strong credentials, but Fellows typically have a broader influence and recognition in their field.

How much do engineering fellows make?

Engineering fellows typically earn between $80,000 and $150,000 annually, depending on experience, industry, and location. Many fellowships are competitive and may include benefits such as mentorship, specialized training, and project-based work.
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Infographic showing various Engineering Fellow job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 91% Full Time, 5% Part Time, 2% Contract, and 1% Nights. Highlights an 83% Physical, 4% Hybrid, and 13% Remote job distribution, with an average salary of $59,022 per year, or $28.4 per hour.

Fellow in Research Engineering, Kempner Institute

Cambridge, MA • On-site

Harvard University
Colleges, Universities, and Professional Schools • 51 - 200 employees
Learn more about Harvard Universityexternal

Full-time

Re-posted 15 days ago

Harvard University rating

8.5

Company rating: 8.5 out of 10

Based on 12 frontline employees who took The Breakroom Quiz

Job description

Position
Details
Title
Fellow in Research Engineering, Kempner Institute
School
Faculty of Arts and Sciences
Department/Area
Kempner Institute for the Study of Natural and Artificial Intelligence
Position Description
The Engineering Fellowship Program at Kempner Institute at Harvard University offers a structured opportunity for recent graduates (fellows) to further their experience in AI/ML engineering. The program offers fellows a comprehensive, hands-on learning experience that prepares them for a successful career in the AI/ML field.
Engineering Fellows will interact directly with a member of the Kempner Institute Research Engineering team to advance their skills and understanding of advanced technologies. This includes developing cutting-edge AI/ML models and datasets; learning how to take advantage of unparalleled computing resources in the academic environment by optimizing AI/ML models including scaling models across a large set of GPUs; building or optimizing LLMs to tackle new, complex tasks; developing new models of brain circuits and function; and learning software engineering best practices including how to develop and disseminate reliable, reproducible open-source AI/ML scientific software packages.
Products resulting from the fellows activities such as code, models, or datasets, may be published on Kempner Institute public channels, including GitHub, Hugging Face, or our Research Blog.
The fellowship program is a full-time (35-hour per week) position. Fellows are appointed for a minimum 6 month commitment, which is typically renewed for an additional 6 month term based on satisfactory performance and mutual interest.
The program is fully on-site, in person in the Kempner Institute, 6th floor, Science and Engineering Complex in Allston, MA. Remote work is not possible in this position. Applicants must be legally eligible to work in the United States. We are not able to provide visa sponsorship for this position.
Basic Qualifications
  • Proficiency in coding (Python) and deep learning frameworks (PyTorch) with a drive to enhance these skills.
  • Familiarity with one of the AI/ML fields like Natural Language Processing, Computer Vision, Reinforcement Learning, generative models, or a strong interest in exploring them.
  • Basic data preprocessing, feature engineering, and model evaluation, or a strong willingness to gain hands-on experience.
  • Eagerness to learn HPC concepts, including parallel computing, distributed systems, and optimization.
  • Analytical skills, problem-solving abilities, and a growth mindset.

Additional Qualifications
Applicants should be within three years of graduation from a bachelor's or master's degree at the time of application.
Special Instructions
Applicants should submit a resume and a cover letter which:
  • Briefly describes your educational background (50 words).
  • Describes a project or experience where you used Python for coding or developing AI/ML models (100 words max).
  • Describes any hands-on experience you have in data preprocessing, feature engineering, and model evaluation (100 words max).
  • Lists any additional skills or technologies you are proficient in (e.g., C++, Julia, AWS, TensorFlow, etc.) (50 words or less).

Cover letters should also include a rating for your:
  • A. Proficiency in Python
  • B. Experience with Deep Learning Frameworks (e.g., PyTorch)
  • C. Familiarity with HPC including running serial or distributed jobs
  • D. Familiarity with AI/ML fields

Using the following ratings:
  • (1) Beginner - little to no experience
  • (2) Intermediate - have used it in projects
  • (3) Advanced - extensive experience and deep understanding in multiple successful projects

Contact Information
Sarah Leinicke
Contact Email
sarah_leinicke@harvard.edu
Salary Range
Minimum Number of References Required
Maximum Number of References Allowed
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