Machine Learning Scientist Internships
Machine learning scientist internships give university students, recent graduates, and early-career switchers hands-on project experience working alongside working data scientists and research engineers, and, at many employers, a path toward a full-time offer. Openings are concentrated across Technology & Software, Social Media, and Media & Entertainment, with TikTok and Lila Sciences among the employers posting roles now.
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Showing 5 of 9+ Machine Learning Scientist Internships
Your Impact at LILA
Lila is building a platform where AI and automation co-evolve to solve hard problems across scientific domains. Within Life Sciences AI, we are developing autonomous-science capabilities for biological systems, spanning multiple biological domains and resolutions, based on multi-modal data and foundation models.
We are seeking a Co-Op, LS AI, ML Scientist for Biology to contribute to cutting-edge research on how to effectively evaluate, guide, and reinforce agentic model behavior in this domain.
This is an opportunity to work alongside Lila scientists on early-stage research in autonomous life science AI. You will help explore reasoning models, evaluation and benchmark datasets, and workflows that connect modern AI methods to real biological questions, gaining hands-on experience in a fast-moving scientific environment.
What You'll Be Building
- Contribute to ML research on reasoning models for biological discovery and autonomous science.
- Explore methods to evaluate, guide, and reinforce agentic model behavior in biological domains.
- Help develop evaluation and benchmark datasets for biological reasoning tasks.
- Analyze multi-modal biological data to identify useful signals for model evaluation and improvement.
- Prototype workflows that connect model reasoning, evaluation, and scientific feedback.
- Communicate findings through code, notebooks, written summaries, and presentations.
What You'll Need to Succeed
- Currently enrolled in a PhD program in Computer Science, Machine Learning, Computational Biology, Bioengineering, or a related quantitative field.
- Research experience in machine learning, AI for science, computational biology, or biological data analysis.
- Strong programming skills in Python and experience with modern ML frameworks such as PyTorch, JAX, or similar tools.
- Experience working with biological, scientific, or multi-modal datasets.
- Interest in reasoning models, agentic systems, evaluation methods, or benchmark design.
- Interest in closed-loop scientific discovery, autonomous labs, or AI systems that interact with experimental feedback.
- Ability to communicate research findings clearly through code, notebooks, written summaries, and presentations.
- Comfort working in a collaborative, cross-disciplinary research environment.
Bonus Points For
- Experience with reasoning models, agentic systems, reinforcement learning, or model evaluation.
- Experience developing benchmarks, evaluation datasets, or model assessment workflows.
- Publications, preprints, talks, posters, or workshop presentations in ML, AI for science, computational biology, or related scientific venues.
About LILA
Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.
LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai.
Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply.
We're All In
Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.
Information you provide during your application process will be handled in accordance with our Candidate Privacy Policy.
A Note to Agencies
Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science's internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.
Your Impact at LILA
Lila is building a platform where AI and automation co-evolve to solve hard problems across scientific domains. Within Life Sciences AI, we are developing autonomous-science capabilities for biological systems, spanning multiple biological domains and resolutions, based on multi-modal data and foundation models.
We are seeking a Co-Op, LS AI, ML Scientist for Biology to contribute to cutting-edge research on how to effectively evaluate, guide, and reinforce agentic model behavior in this domain.
This is an opportunity to work alongside Lila scientists on early-stage research in autonomous life science AI. You will help explore reasoning models, evaluation and benchmark datasets, and workflows that connect modern AI methods to real biological questions, gaining hands-on experience in a fast-moving scientific environment.
What You'll Be Building
- Contribute to ML research on reasoning models for biological discovery and autonomous science.
- Explore methods to evaluate, guide, and reinforce agentic model behavior in biological domains.
- Help develop evaluation and benchmark datasets for biological reasoning tasks.
- Analyze multi-modal biological data to identify useful signals for model evaluation and improvement.
- Prototype workflows that connect model reasoning, evaluation, and scientific feedback.
- Communicate findings through code, notebooks, written summaries, and presentations.
What You'll Need to Succeed
- Currently enrolled in a PhD program in Computer Science, Machine Learning, Computational Biology, Bioengineering, or a related quantitative field.
- Research experience in machine learning, AI for science, computational biology, or biological data analysis.
- Strong programming skills in Python and experience with modern ML frameworks such as PyTorch, JAX, or similar tools.
- Experience working with biological, scientific, or multi-modal datasets.
- Interest in reasoning models, agentic systems, evaluation methods, or benchmark design.
- Interest in closed-loop scientific discovery, autonomous labs, or AI systems that interact with experimental feedback.
- Ability to communicate research findings clearly through code, notebooks, written summaries, and presentations.
- Comfort working in a collaborative, cross-disciplinary research environment.
Bonus Points For
- Experience with reasoning models, agentic systems, reinforcement learning, or model evaluation.
- Experience developing benchmarks, evaluation datasets, or model assessment workflows.
- Publications, preprints, talks, posters, or workshop presentations in ML, AI for science, computational biology, or related scientific venues.
About LILA
Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.
LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai.
Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply.
We're All In
Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.
Information you provide during your application process will be handled in accordance with our Candidate Privacy Policy.
A Note to Agencies
Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science's internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.
Machine Learning Scientist Internship Market
Who's Hiring
Top Industries Hiring
- Technology & Software
- Social Media
- Media & Entertainment
Tips for Your Machine Learning Scientist Internship Search
Apply in the fall for summer roles
Large tech companies and research labs open summer machine learning scientist internship applications as early as August or September of the prior year. Waiting until spring means the most competitive cohort programs are already closed. Set a reminder at the start of each fall semester and submit applications to structured programs before October.
Build a project portfolio before you apply
Hiring teams for machine learning scientist interns have little work history to evaluate, so your portfolio does the work your resume can't. Document two or three end-to-end projects with the datasets, frameworks, and outcomes, and publish the code to a GitHub repository or share a Kaggle notebook so recruiters can assess your thinking directly.
Work your campus network and apply directly at the same time
Campus career fairs surface structured internship programs tied to your university, and professors or career center staff often know which employers recruit from your school before roles appear publicly. Applying directly to companies running smaller cohorts alongside campus activity widens the pool you reach and increases your chances of landing interviews across both channels.
Practice your technical screen out loud before interviewing
Machine learning scientist intern screens typically combine coding questions on data structures and algorithms with conceptual questions on probability, statistics, and model design. Practice explaining your reasoning while you work through problems, not just arriving at the answer, since interviewers weigh how you think as much as whether your solution is correct.
Target structured ML internship programs early
Large technology and research organizations run dedicated machine learning research internship cohorts designed for students new to applied ML. These programs provide structured mentorship, defined project scopes, and clear conversion processes. They recruit well ahead of the general market and fill their cohorts fast, so identify the programs that match your background and apply in the first wave.
Set your work-type filter before you start
On-site roles are 100% of the machine learning scientist internships listed here. Decide what you can realistically commit to before you start reviewing listings, then filter by location and work type on Migrate Mate so you're only evaluating roles you can actually accept and not sorting through openings that don't fit your situation.
Machine Learning Scientist Internships: Frequently Asked Questions
How do I get a machine learning scientist internship?
Lead with coursework and personal projects rather than work history, since hiring teams expect limited experience at the intern level. The concrete artifact that gives recruiters something to assess is a portfolio of documented ML projects, ideally with linked code or a GitHub repository showing model builds, experiments, and results. Combine direct applications with campus career fairs, where recruiters often move faster for students they meet in person.
Can a machine learning scientist internship turn into a full-time job?
Many employers extend return offers to strong interns, but conversion is never guaranteed. What actually drives it for machine learning scientist interns is performance on real research or engineering work, available headcount on the team, and whether the return-offer window aligns with your graduation timeline. Position for one by delivering on your project scope and asking early about the process, without counting on it as your only plan.
When should I apply for machine learning scientist internships?
Earlier than most candidates expect. Large tech and research employers open summer internship applications the preceding fall, sometimes as early as August or September. Smaller companies and co-op programs post much closer to their start dates, so openings appear year-round. Checking regularly and applying as soon as a role opens is more effective than waiting for a single recruiting season.
Are machine learning scientist internships paid?
Most professional machine learning scientist internships in the U.S. are paid. Compensation varies by company size, industry, and location, and listings show it where the employer chooses to disclose it. Research-focused roles at universities or nonprofits sometimes offer stipends rather than salaries, so read each listing carefully to understand the compensation structure before applying.
What should a machine learning scientist internship resume include?
Lead with two or three complete, documented projects rather than work history. Each project should name the tools and frameworks used, the problem you solved, and where a recruiter can see the work, such as a linked GitHub repository or a published Kaggle notebook. Add relevant coursework in machine learning, statistics, and programming, list any published papers or research contributions, and keep the whole document to one page.
Are there remote machine learning scientist internships?
Yes. Remote and hybrid roles make up 0% of the machine learning scientist internship listings here, with the rest on-site. Remote cohorts fill fast because they attract a wider applicant pool, so apply early once you find a role that fits. Filter by work type at the start of your search so you're only reviewing listings you can actually accept.
What is a machine learning research internship versus a machine learning engineer internship?
A research internship focuses on novel experimentation, publishing findings, and advancing model architectures, typically within a dedicated research team or lab. An engineering internship emphasizes building, deploying, and scaling ML systems in production. Both are competitive and recruit early. Knowing which track fits your background and goals helps you target the right roles and frame your projects accordingly when you apply.
Can international students get machine learning scientist internships?
Yes. F-1 students can intern through CPT while enrolled or through OPT work authorization after finishing a degree, and the employer does not have to file anything for either, so many companies are open to international interns. Confirm your eligibility and timing with your university's international student office before accepting an offer.
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