Machine Learning Research Jobs
Machine Learning Research jobs are open across technology, healthcare, finance, and defense, from research scientist to principal researcher and research director, with specializations in deep learning, natural language processing, and computer vision. Find a role that fits from the openings below and apply directly.
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Join the Future of Commerce with Whatnot!
Whatnot is the largest live shopping platform in North America and Europe to buy, sell, and discover the things you love. Whether it's trading cards, fashion, electronics, or live plants, our sellers are building real businesses across hundreds of categories. We're building live commerce at a scale that's never been done in the West, and there's no playbook to copy. The people here are shaping how an entirely new industry develops.
As a remote co-located team, we're inspired by our values and anchored in hubs across the US, UK, Ireland, Poland, Germany, and Australia. We move fast, stay close to our users, and focus on the work that drives the most impact.
We're one of the fastest growing marketplaces and were recently named the #1 Best Startup Employer in America by Forbes. Check out the latest Whatnot updates on our news and engineering blogs and join us as we enable anyone to turn their passion into a business and bring people together through commerce.
Role
Lead research projects across the marketplace dynamics problem area: simulation, auction and allocation mechanics, long-term objective modeling, exploration and information value, or marketplace experimentation methods
Take ideas from hypothesis to production: literature review, prototyping, offline validation, shadow testing, and online experiments shipped through partner teams in Discovery and the Seller org
Build models of how the marketplace behaves as a system: learned simulators that predict segment-level effects of ranking and policy changes, and surrogate models of long-term marketplace outcomes
Model Whatnot's actual market mechanics: auction and bidding dynamics, and discovery exposure allocation as a portfolio problem, including allocation to rising sellers
Advance how a multi-sided live marketplace evaluates changes: off-policy evaluation, switchback and interference-robust experiment designs, and variance reduction
Contribute to Whatnot's external technical presence through publications, open-source work, and public benchmarks
NYC Based:
Team members in this role are required to be within commuting distance (50 miles) of our New York City hub.
You
Curious about who thrives at Whatnot? We’ve found that embodying a low ego, growth mindset, and high-impact drive goes a long way here. As our next Machine Learning Engineer you should have:
5+ years of industry experience building and deploying ML models to solve user problems at scale
Depth in at least one of: recommendation systems, causal inference, off-policy evaluation, reinforcement learning and bandits, auction or mechanism design, or marketplace experimentation
A track record of applying scientific methods to solve real-world problems on consumer-scale data
Advanced proficiency in Python, SQL, and common ML frameworks like PyTorch, XGBoost, etc
Strong grounding in applied statistics, experiment design and theoretical machine learning
Strong communication and leadership skills; ability to influence roadmaps and align cross-functional teams in a remote environment
Preferred Qualifications:
Experience in two-sided marketplaces, ads and auction systems, or pricing
Experience building simulators or economic models of platform behavior
Compensation
For Full-Time (Salary) US-based applicants: $207,000/year to $290,000X/year + benefits + equity.
The salary range may be inclusive of several levels that would be applicable to the position. Final salary will be based on a number of factors including, level, relevant prior experience, skills, and expertise. This range is only inclusive of base salary, not benefits (more details below) or equity.
Benefits
Flexible Time off Policy and Company-wide Holidays (including a spring and winter break)
Health Insurance options including Medical, Dental, Vision
Work From Home Support
Home office setup allowance
Monthly allowance for cell phone and internet
Care benefits
Monthly allowance for wellness
Annual allowance towards Childcare
Lifetime benefit for family planning, such as adoption or fertility expenses
Retirement; 401k offering for Traditional and Roth accounts in the US (employer match up to 4% of base salary) and Pension plans internationally
Monthly allowance to dogfood the app
Parental Leave
16 weeks of paid parental leave + one month gradual return to work *company leave allowances run concurrently with country leave requirements which take precedence.
Please note: Whatnot will only contact you through official @whatnot.com email addresses. If you see an email impersonating a Whatnot recruiter, please disregard and report it as spam.
EOE
Whatnot is proud to be an Equal Opportunity Employer. We value diversity, and we do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, parental status, disability status, or any other status protected by local law. We believe that our work is better and our company culture is improved when we encourage, support, and respect the different skills and experiences represented within our workforce.
Compensation Range: $207K - $290K
Join the Future of Commerce with Whatnot!
Whatnot is the largest live shopping platform in North America and Europe to buy, sell, and discover the things you love. Whether it's trading cards, fashion, electronics, or live plants, our sellers are building real businesses across hundreds of categories. We're building live commerce at a scale that's never been done in the West, and there's no playbook to copy. The people here are shaping how an entirely new industry develops.
As a remote co-located team, we're inspired by our values and anchored in hubs across the US, UK, Ireland, Poland, Germany, and Australia. We move fast, stay close to our users, and focus on the work that drives the most impact.
We're one of the fastest growing marketplaces and were recently named the #1 Best Startup Employer in America by Forbes. Check out the latest Whatnot updates on our news and engineering blogs and join us as we enable anyone to turn their passion into a business and bring people together through commerce.
Role
Lead research projects across the marketplace dynamics problem area: simulation, auction and allocation mechanics, long-term objective modeling, exploration and information value, or marketplace experimentation methods
Take ideas from hypothesis to production: literature review, prototyping, offline validation, shadow testing, and online experiments shipped through partner teams in Discovery and the Seller org
Build models of how the marketplace behaves as a system: learned simulators that predict segment-level effects of ranking and policy changes, and surrogate models of long-term marketplace outcomes
Model Whatnot's actual market mechanics: auction and bidding dynamics, and discovery exposure allocation as a portfolio problem, including allocation to rising sellers
Advance how a multi-sided live marketplace evaluates changes: off-policy evaluation, switchback and interference-robust experiment designs, and variance reduction
Contribute to Whatnot's external technical presence through publications, open-source work, and public benchmarks
NYC Based:
Team members in this role are required to be within commuting distance (50 miles) of our New York City hub.
You
Curious about who thrives at Whatnot? We’ve found that embodying a low ego, growth mindset, and high-impact drive goes a long way here. As our next Machine Learning Engineer you should have:
5+ years of industry experience building and deploying ML models to solve user problems at scale
Depth in at least one of: recommendation systems, causal inference, off-policy evaluation, reinforcement learning and bandits, auction or mechanism design, or marketplace experimentation
A track record of applying scientific methods to solve real-world problems on consumer-scale data
Advanced proficiency in Python, SQL, and common ML frameworks like PyTorch, XGBoost, etc
Strong grounding in applied statistics, experiment design and theoretical machine learning
Strong communication and leadership skills; ability to influence roadmaps and align cross-functional teams in a remote environment
Preferred Qualifications:
Experience in two-sided marketplaces, ads and auction systems, or pricing
Experience building simulators or economic models of platform behavior
Compensation
For Full-Time (Salary) US-based applicants: $207,000/year to $290,000X/year + benefits + equity.
The salary range may be inclusive of several levels that would be applicable to the position. Final salary will be based on a number of factors including, level, relevant prior experience, skills, and expertise. This range is only inclusive of base salary, not benefits (more details below) or equity.
Benefits
Flexible Time off Policy and Company-wide Holidays (including a spring and winter break)
Health Insurance options including Medical, Dental, Vision
Work From Home Support
Home office setup allowance
Monthly allowance for cell phone and internet
Care benefits
Monthly allowance for wellness
Annual allowance towards Childcare
Lifetime benefit for family planning, such as adoption or fertility expenses
Retirement; 401k offering for Traditional and Roth accounts in the US (employer match up to 4% of base salary) and Pension plans internationally
Monthly allowance to dogfood the app
Parental Leave
16 weeks of paid parental leave + one month gradual return to work *company leave allowances run concurrently with country leave requirements which take precedence.
Please note: Whatnot will only contact you through official @whatnot.com email addresses. If you see an email impersonating a Whatnot recruiter, please disregard and report it as spam.
EOE
Whatnot is proud to be an Equal Opportunity Employer. We value diversity, and we do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, parental status, disability status, or any other status protected by local law. We believe that our work is better and our company culture is improved when we encourage, support, and respect the different skills and experiences represented within our workforce.
Compensation Range: $207K - $290K
Machine Learning Research Jobs by Experience Level
Top Cities Hiring Machine Learning Researchs
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Find JobsMachine Learning Research Job Market
Who's Hiring
- Apple35
- Scale AI17
- ByteDance6
- Meta5
- Achira5A
Top Industries Hiring
- Technology & Software25
- Electronics & Hardware19
- Artificial Intelligence12
- Investment & Asset Management7
- Banking & Financial Services5
What Employers Look For
The qualifications that appear most often in machine learning research jobs.
- PhD or MS in computer science, statistics, or a related quantitative field
- Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow
- Experience designing and running controlled experiments on large-scale datasets
- Published or peer-reviewed research in a relevant machine learning subfield
- Ability to implement and evaluate state-of-the-art models from recent literature
- Strong written and verbal communication skills for presenting research findings internally
Tips for Your Machine Learning Research Job Search
Lead with reproducible research outputs
Your resume should link to public repositories, preprints, or published papers so hiring teams can verify your work. Listing model names or benchmark scores without evidence won't move you past an initial screening for a research role.
Apply early to roles that fit
Migrate Mate lists machine learning research openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Filter openings by research stage focus
Some teams want applied researchers who ship models to production, while others want fundamental researchers who write papers. Read job descriptions carefully for phrases like 'product impact' versus 'novel contributions' to avoid applying to roles that don't match your actual work style.
Tailor your PhD or postdoc framing carefully
Industry hiring managers value academic credentials but worry about translation to shipping timelines. Frame dissertation work around the problem you solved and its practical scope, not the methodological novelty, so it reads as relevant to a product-adjacent research team.
Prepare a research presentation for onsite rounds
Most machine learning research onsites include a whiteboard or slide presentation of a past project. Choose a paper or project where you made a non-obvious design decision, because interviewers probe trade-offs, not just results.
Negotiate compute and publication rights separately
Salary negotiation in research roles often matters less than access to GPU clusters and the company's policy on publishing findings. Clarify publication approval timelines and compute allocation during the offer stage, not after you've accepted.
Machine Learning Research Jobs: Frequently Asked Questions
Which companies are hiring the most machine learning researchers?
The companies hiring the most machine learning researchers right now include Apple, Scale AI, and ByteDance, with the largest share of openings in California, Washington, and New York, based on current listings on Migrate Mate as of September 2026. Demand is concentrated at large technology companies, AI-focused startups, and research labs within healthcare and defense organizations.
How many machine learning research jobs are remote?
About 33% of machine learning research openings are fully remote or hybrid as of September 2026, reflecting the field's strong orientation toward asynchronous collaboration and distributed teams. Roles focused on natural language processing and data-centric research tend to be the most remote-friendly, while positions requiring access to specialized hardware or on-site lab infrastructure are more likely to require in-person presence.
How do you become a machine learning researcher?
Most machine learning researchers begin by earning a graduate degree in computer science, statistics, or a closely related field, where coursework covers optimization, probabilistic modeling, and neural network architectures. Building a public record of work through open-source contributions, competition placements, or preprints helps demonstrate capability. Applying to research internships at companies or labs while still in school is one of the most direct paths into a full-time research role.
Can you get a machine learning research job without a PhD?
Yes, candidates with a strong master's degree and a portfolio of published or well-documented independent research do get hired into machine learning research roles, particularly at applied research teams and AI product companies. What matters most is evidence of original thinking: a paper, a compelling open-source project, or a detailed write-up of a novel approach to a real problem. Roles labeled 'research engineer' or 'applied scientist' are often more accessible entry points than positions titled 'research scientist.'
What does the machine learning research interview process look like?
The process typically opens with a recruiter screen focused on background and research interests, followed by a technical phone screen covering machine learning concepts, coding in Python, and sometimes probability or statistics fundamentals. Onsite rounds usually include a research presentation of past work, one or two coding interviews, and a deep-dive conversation with senior researchers on your methods and trade-offs. Some companies also include a take-home research problem or a paper review discussion.
Where can I find and apply to machine learning research jobs?
You can find and apply to machine learning research jobs on Migrate Mate, which lists current openings from companies across the United States. Find roles that match your background and research focus, then apply directly to each listing. New positions are added regularly, so checking back often gives you a better chance of catching openings before they close.
See All 156+ Machine Learning Research Jobs
Find roles that match your experience and apply in just a few clicks.
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