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Machine Learning Fellow Jobs (NOW HIRING)

Arcadia Science

Platform Fellow

Emeryville, CA · On-site

$8K - $12K/mo

We are seeking a Platform Fellow to join our Platform team for a 3-month fellowship. Platform uses ... PhD in computational biology, statistics, machine learning, genetics, evolutionary biology, or a ...

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$59K

$83.5K

How much do machine learning fellow jobs pay per year?

As of May 31, 2026, the average yearly pay for machine learning 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 are the key skills and qualifications needed to thrive as a Machine Learning Fellow, and why are they important?

A Machine Learning Fellow typically needs a strong background in mathematics, statistics, and programming (Python, R, or similar), often supported by an advanced degree in computer science, data science, or related fields. Familiarity with machine learning frameworks (such as TensorFlow or PyTorch), cloud platforms, and data analysis tools is essential. Critical thinking, curiosity, and effective communication help fellows solve complex problems and share findings with technical and non-technical audiences. These skills enable effective model development, impactful research, and successful collaboration in interdisciplinary teams.

What are some typical projects or tasks a Machine Learning Fellow can expect to work on during their fellowship?

As a Machine Learning Fellow, you can expect to engage in hands-on projects such as developing and optimizing machine learning models, analyzing large datasets, and collaborating with research scientists or engineers. Fellows often participate in exploratory research, contribute to publications, or assist with deploying models into production environments. The role typically involves regular team meetings, code reviews, and opportunities to present your findings to both technical and non-technical audiences, offering valuable experience for future career advancement.

What is a Machine Learning Fellow?

A Machine Learning Fellow is typically an early-career or advanced student involved in a structured fellowship program focused on machine learning research or applications. These fellowships provide opportunities to work on real-world projects, collaborate with experts, and deepen knowledge in areas such as data analysis, model development, and artificial intelligence. Fellows often contribute to research papers, attend workshops, and gain hands-on experience with cutting-edge technologies, preparing them for future roles in academia or industry.

What is the difference between Machine Learning Fellow vs Data Scientist?

AspectMachine Learning FellowData Scientist
Required CredentialsAdvanced degree in CS, ML, or related field; research experienceDegree in CS, statistics, or related; some roles prefer experience in analytics
Work EnvironmentResearch-focused, academic or corporate R&D teamsBusiness analytics, product development, or consulting teams
Employer & Industry UsageUniversities, research labs, tech companiesTech firms, finance, healthcare, e-commerce
Search & Comparison IntentFocus on research, advanced ML projectsData analysis, insights, and modeling

While both roles involve working with data and machine learning, a Machine Learning Fellow typically focuses on research and developing new algorithms, often in academic or R&D settings. Data Scientists apply ML techniques to solve business problems, analyze data, and generate insights. The roles overlap in skills and tools but differ mainly in their primary focus and work environment.

More about Machine Learning Fellow jobs
What job categories do people searching Machine Learning Fellow jobs look for? The top searched job categories for Machine Learning Fellow jobs are:
Machine Learning Fellow jobs near you
Infographic showing various Machine Learning Fellow job openings in the United States as of May 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $59,022 per year, or $28.4 per hour.
Platform Fellow

Platform Fellow

Arcadia Science

Emeryville, CA • On-site

$8K - $12K/mo

Full-time

Posted 9 days ago

Job description

A Bit About Us
We are Arcadia Science, an evolutionary biology company founded and led by scientists. Our mission is to turn natural innovations into real-world solutions by developing systematic and quantitative approaches to leveraging biology for therapeutics R&D. We share our research as openly as possible to accelerate discovery and make our work broadly useful.
The Opportunity
We're closing the gap between biological data and biological understanding. Our Platform team builds evolution-aware statistical and ML methods that use phylogeny as structure, making inference more reliable and more generalizable. The result is shared infrastructure that supports research across Arcadia. Read more about our work through our publications.
We are seeking a Platform Fellow to join our Platform team for a 3-month fellowship. Platform uses phylogeny to guide the development of many of our statistical models, machine learning tools, and evolutionary frameworks, powering research across Arcadia. This is an ideal opportunity for scientists in the final stages of their PhD or postdoc training who want to experience industry research in an open science environment, or for researchers looking to apply their quantitative skills to evolutionary biology in new ways.
Platform Fellows will work closely with our computational teams on projects at the intersection of machine learning, statistics, and evolutionary biology. Fellows will contribute to a defined project with the goal of publishing their work openly by the end of the fellowship.
Areas of Focus
We are looking for candidates with expertise in one or more of the following areas:
- Probabilistic modeling and statistical inference
- Supervised and unsupervised learning for high-dimensional biological data
- Interpretability methods development for ML models
- Phylogenetic inference and evolutionary modeling
- Comparative and evolutionary genomics across species
- Quantitative and population genetics, including human genetics
- Analysis of natural selection, adaptation, and trait evolution
- Statistical and machine learning approaches to quantitative genetics
What You'll Do
  • Work with Platform team members on a defined computational project aligned with Arcadia's research goals
  • Independently manage your fellowship work, troubleshooting issues as they arise
  • Keep detailed documentation for all workflows, code, and analyses
  • Publish results openly and share data, code, and methods by the end of the fellowship

Qualifications
  • PhD in computational biology, statistics, machine learning, genetics, evolutionary biology, or a related quantitative field
  • Strong programming skills (Python and/or R required; familiarity with bash and version control)
  • Experience with statistical modeling, machine learning, or phylogenetic methods
  • Ability to work independently while collaborating with cross-functional teams
  • Strong written and verbal communication skills

$8,000 - $12,000 a month
Fellows will receive a competitive monthly stipend. Travel and relocation support is available for candidates relocating temporarily to the Bay Area.
What We Look For
The first thing we look for is technical talent at the leading edge of a field. To us, this means independent and generative scientists doing basic research that has the potential to produce evidence challenging standard practices. This is true for all of our roles: whether someone is an individual contributor or a people manager, we want researchers pushing their field forward.
For this role in particular, we are looking for scientists who want to apply quantitative methods to biological questions across the tree of life. The ideal candidate is comfortable with ambiguity, moves quickly, and communicates clearly through writing. Our team is flexible and often needs to pivot to match the pace of development at the cutting edge of multiple fields. Writing independently is crucial since we share everything we think is valuable to the community and will help us move our research forward.
We try to develop novel approaches for every aspect of our science, and everyone on our team is expected to innovate in their domain. This is not easy, and won't be for everyone. We believe a culture where this is possible stems from how we act.
Application Process
Interested applicants should apply using the link. We expect to review applications at a monthly cadence.
Due to anticipated volume, we may not be able to respond to all applicants. If you advance beyond initial review, we will let you know.

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