AI/ML Researcher Jobs
AI/ML Researcher jobs are open from new-grad to principal level across technology, healthcare, finance, and defense, with common specializations in natural language processing, computer vision, and reinforcement learning. See the openings below and apply to the ones that match your experience.
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We build frontier foundation models that power intelligent experiences at Apple. Our team works across the full training lifecycle: including pre-training foundation models, and developing mid-training approaches that bridge general capability and task-specific performance. What makes our work distinct is that we're engineering models specifically for Apple silicon and optimized for experiences that are private, personal, and deeply integrated into the OS. We're solving frontier problems in reward modeling to resist reward hacking, handling sparse and delayed rewards in agentic settings, and aligning models reliably across the spectrum from open-ended creative tasks to precise, action-taking workflows. If you're drawn to hard problems where the research and the product are inseparable, this is the team.
Description
We believe that the most interesting problems in deep learning research arise when we try to apply learning to real-world use cases, and this is also where the most important breakthroughs come from. You will work with a close-knit and fast growing team of world-class engineers and scientists to tackle some of the most challenging problems in foundation models and deep learning. Further, you will have opportunities to identify and develop novel applications of deep learning in Apple products. You will see your ideas improve the experience of billions of users.
Minimum Qualifications
- Demonstrated expertise in deep learning with publication record in relevant conferences (e.g., NeurIPS, ICML, ICLR, COLM, ACL, NAACL, EMNLP, ACL) or a track record in applying deep learning techniques to products
- Proficient programming skills in Python and one of the deep learning toolkits such as JAX, PyTorch, or Tensorflow
- Ability to work in a collaborative environment.
- Code large language models.
- PhD, or equivalent practical experience, in Computer Science, or related technical field.
Preferred Qualifications
- Reinforcement learning, on-policy distillation.
- Post-training, mid-training large language models.
- LLM context lengthening.
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $150,400 and $277,600, and your base pay will depend on your skills, qualifications, experience, and location.
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple’s Employee Stock Purchase Plan. You’ll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses — including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.
Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant
At Apple, we believe accessibility is a fundamental human right. You’ll find that idea reflected in everything here — in our culture, our benefits and our digital tools. By welcoming as many perspectives as possible, we help you build a career where you feel like you belong.
Learn about accessibility in Apple’s workplace
Learn about reasonable accommodations for job applicants
Apple accepts applications to this posting on an ongoing basis.
We build frontier foundation models that power intelligent experiences at Apple. Our team works across the full training lifecycle: including pre-training foundation models, and developing mid-training approaches that bridge general capability and task-specific performance. What makes our work distinct is that we're engineering models specifically for Apple silicon and optimized for experiences that are private, personal, and deeply integrated into the OS. We're solving frontier problems in reward modeling to resist reward hacking, handling sparse and delayed rewards in agentic settings, and aligning models reliably across the spectrum from open-ended creative tasks to precise, action-taking workflows. If you're drawn to hard problems where the research and the product are inseparable, this is the team.
Description
We believe that the most interesting problems in deep learning research arise when we try to apply learning to real-world use cases, and this is also where the most important breakthroughs come from. You will work with a close-knit and fast growing team of world-class engineers and scientists to tackle some of the most challenging problems in foundation models and deep learning. Further, you will have opportunities to identify and develop novel applications of deep learning in Apple products. You will see your ideas improve the experience of billions of users.
Minimum Qualifications
- Demonstrated expertise in deep learning with publication record in relevant conferences (e.g., NeurIPS, ICML, ICLR, COLM, ACL, NAACL, EMNLP, ACL) or a track record in applying deep learning techniques to products
- Proficient programming skills in Python and one of the deep learning toolkits such as JAX, PyTorch, or Tensorflow
- Ability to work in a collaborative environment.
- Code large language models.
- PhD, or equivalent practical experience, in Computer Science, or related technical field.
Preferred Qualifications
- Reinforcement learning, on-policy distillation.
- Post-training, mid-training large language models.
- LLM context lengthening.
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $150,400 and $277,600, and your base pay will depend on your skills, qualifications, experience, and location.
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple’s Employee Stock Purchase Plan. You’ll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses — including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.
Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant
At Apple, we believe accessibility is a fundamental human right. You’ll find that idea reflected in everything here — in our culture, our benefits and our digital tools. By welcoming as many perspectives as possible, we help you build a career where you feel like you belong.
Learn about accessibility in Apple’s workplace
Learn about reasonable accommodations for job applicants
Apple accepts applications to this posting on an ongoing basis.
AI/ML Researcher Jobs by Experience Level
Top Cities Hiring AI/ML Researchers
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Find AI/ML Researcher JobsAI/ML Researcher Job Market
Who's Hiring
- Apple21
- JPMorganChase17
- Amazon Web Services9
- Booz Allen Hamilton6
- Accenture Federal Services4
Top Industries Hiring
- Technology & Software13
- Banking & Financial Services8
- Investment & Asset Management8
- Electronics & Hardware6
- Fintech4
What Employers Look For
The qualifications that appear most often in AI/ML researcher jobs.
- PhD or Master's degree 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 experiments with large-scale datasets
- Familiarity with transformer architectures and modern foundation model techniques
- Published research or demonstrated contributions to the machine learning community
- Ability to communicate technical findings clearly to cross-functional stakeholders
Tips for Your AI/ML Researcher Job Search
Tailor your resume to the method
Hiring managers scan for the specific modeling approaches you've used, not just 'machine learning.' Name the algorithms, architectures, and frameworks you applied and pair each with a concrete outcome, such as a reduction in error rate or an improvement in inference speed.
Link every project to a public artifact
Reviewers for research roles expect to verify your work. Attach a preprint, a conference paper, a public repository, or a technical blog post to each major project on your resume so they can read beyond the bullet point.
Apply early to roles that fit
Migrate Mate lists ai/ml researcher openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Filter by research versus applied focus
Job titles overlap heavily in this field. Read the responsibilities section carefully to distinguish pure research roles, which expect publications and novel contributions, from applied science roles, which weight production deployment and cross-functional delivery. Applying to the wrong type wastes your time.
Prepare a paper walkthrough for interviews
Many ai/ml researcher interviews include a research presentation or a deep dive into a paper you authored or admire. Practice explaining your methodological choices, the limitations of your approach, and what you would do differently, not just the results.
Negotiate compute and data access, not just pay
Research velocity depends on GPU budgets, proprietary datasets, and cloud credits. Ask specifically about compute allocation policies, internal dataset access, and whether the team has a publication approval process before accepting any offer.
AI/ML Researcher Jobs: Frequently Asked Questions
Which companies are hiring the most ai/ml researchers?
The companies hiring the most ai/ml researchers right now include Apple, JPMorganChase, and Amazon Web Services, with the largest share of openings in California, Texas, and Virginia, based on current listings on Migrate Mate as of November 2026. Demand is concentrated in technology, cloud infrastructure, and enterprise software companies investing in generative AI and foundation model development.
How many ai/ml researcher jobs are remote?
About 71% of ai/ml researcher openings are fully remote or hybrid as of November 2026, making it one of the more flexible research disciplines in terms of location. Roles focused on natural language processing and data-centric research tend to offer the highest share of fully remote options, while hardware-adjacent or lab-based positions more often require on-site presence.
How do you become an ai/ml researcher?
Most ai/ml researchers build a foundation through a graduate degree in computer science, mathematics, or a closely related field, then develop applied skills through thesis research, internships, or open-source contributions. Publishing or presenting at recognized machine learning venues strengthens your candidacy significantly. Building a public portfolio of reproducible experiments and contributing to collaborative research projects rounds out the profile employers look for.
Can you get an ai/ml researcher job with little or no experience?
Breaking in with limited experience is possible but requires a deliberate portfolio strategy. Focus on completing end-to-end projects that involve a clearly stated problem, a novel or well-justified methodological choice, and a reproducible result. Contributing to open-source research codebases, co-authoring papers with academics, or completing a research internship at a company or national lab are the most direct paths employers recognize when your formal experience is thin.
What does the ai/ml researcher interview process look like?
The process typically opens with a recruiter screen followed by a technical phone interview covering machine learning fundamentals, probability, and coding. A research presentation or paper deep-dive is common at the next stage, where you walk through your own work or a selected paper and field detailed methodological questions. Final rounds usually include a full-day set of interviews covering research vision, system design for ML pipelines, and cross-functional collaboration scenarios.
Where can I find and apply to ai/ml researcher jobs?
You can find and apply to ai/ml researcher jobs on Migrate Mate, which lists current openings from across the United States in one place. Search the listings to find roles that match your background and apply directly to each one that fits.
See All 170+ AI/ML Researcher Jobs
Find roles that match your experience and apply in just a few clicks.
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