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.
Find JobsLooking for remote work? View remote machine learning research jobs →Student or new grad? View machine learning research internships →Overview
Showing 5 of 157+ Machine Learning Research jobs
Available in 16 locations See allSee all
Research Engineer — Agentic Audio ML Workflows
Description
At Bose Corporation, we believe sound is the most powerful force on earth — and for over 60 years, we have been a company built on innovation, excellence, and independence. Privately owned, fiercely customer-focused, and driven by our values, we continue to lead industries and transform lives through sound.
Today, Bose Corporation is entering an exciting new era. Across multiple global Business Units and Global Functions, we are shaping the future of audio technology, automotive, luxury, and premium experiences. We invite you to join us in this transformation.
Job Description
Description
The goal of our team is to develop novel AI-powered audio processing algorithms. The twist is that our algorithms must run in real time, on physical devices. You will build agentic tools and automation that make our audio ML research dramatically faster and more reproducible. The focus is on orchestrating training, evaluation, experimentation, and reporting across many types of AI models, using on-premise and/or cloud GPU infrastructure.
Bose has a strong history of combining creative thinking with cutting-edge audio technology. We are looking for people passionate about machine learning and audio to help shape what comes next.
Responsibilities
- Design and implement agentic workflows that plan, launch, monitor, diagnose, and summarize audio ML experiments, with appropriate human oversight.
- Automate data preparation, model training, hyperparameter searches, benchmarking, regression testing, and result comparison across research projects.
- Build reliable tooling for on-premises GPU scheduling and utilization, experiment tracking, artifact management, and reproducible execution.
- Connect LLM-powered agents to scripts, repositories, datasets, evaluation suites, and compute resources through well-defined tools and safeguards.
- Partner with audio researchers to turn repetitive experiments into reusable workflows and clear dashboards or reports.
Education
Degree in computer science, machine learning, electrical engineering, or a related field, or equivalent practical experience.
Skills
- Strong software engineering and Python skills, with hands-on experience building LLM agents or agentic workflows that invoke tools and execute multi-step tasks.
- Comfortable tackling complex, open-ended problems where you have to experiment with and discard many ideas before finding the right solution.
- Experience with ML training and evaluation pipelines, PyTorch, experiment tracking, and automated testing.
- Practical experience operating on-premises Linux GPU machines, including CUDA environments, containers, job scheduling, and resource monitoring.
- Working knowledge of audio ML and DSP, including common datasets, audio metrics, and model development workflows.
- Experience with orchestration frameworks, distributed training, CI/CD, or MLOps is a plus.
- Ability to build reliable, observable systems and communicate effectively with research scientists.
Available in 16 locations See allSee all
Research Engineer — Agentic Audio ML Workflows
Description
At Bose Corporation, we believe sound is the most powerful force on earth — and for over 60 years, we have been a company built on innovation, excellence, and independence. Privately owned, fiercely customer-focused, and driven by our values, we continue to lead industries and transform lives through sound.
Today, Bose Corporation is entering an exciting new era. Across multiple global Business Units and Global Functions, we are shaping the future of audio technology, automotive, luxury, and premium experiences. We invite you to join us in this transformation.
Job Description
Description
The goal of our team is to develop novel AI-powered audio processing algorithms. The twist is that our algorithms must run in real time, on physical devices. You will build agentic tools and automation that make our audio ML research dramatically faster and more reproducible. The focus is on orchestrating training, evaluation, experimentation, and reporting across many types of AI models, using on-premise and/or cloud GPU infrastructure.
Bose has a strong history of combining creative thinking with cutting-edge audio technology. We are looking for people passionate about machine learning and audio to help shape what comes next.
Responsibilities
- Design and implement agentic workflows that plan, launch, monitor, diagnose, and summarize audio ML experiments, with appropriate human oversight.
- Automate data preparation, model training, hyperparameter searches, benchmarking, regression testing, and result comparison across research projects.
- Build reliable tooling for on-premises GPU scheduling and utilization, experiment tracking, artifact management, and reproducible execution.
- Connect LLM-powered agents to scripts, repositories, datasets, evaluation suites, and compute resources through well-defined tools and safeguards.
- Partner with audio researchers to turn repetitive experiments into reusable workflows and clear dashboards or reports.
Education
Degree in computer science, machine learning, electrical engineering, or a related field, or equivalent practical experience.
Skills
- Strong software engineering and Python skills, with hands-on experience building LLM agents or agentic workflows that invoke tools and execute multi-step tasks.
- Comfortable tackling complex, open-ended problems where you have to experiment with and discard many ideas before finding the right solution.
- Experience with ML training and evaluation pipelines, PyTorch, experiment tracking, and automated testing.
- Practical experience operating on-premises Linux GPU machines, including CUDA environments, containers, job scheduling, and resource monitoring.
- Working knowledge of audio ML and DSP, including common datasets, audio metrics, and model development workflows.
- Experience with orchestration frameworks, distributed training, CI/CD, or MLOps is a plus.
- Ability to build reliable, observable systems and communicate effectively with research scientists.
Machine Learning Research Jobs by Experience Level
Top Cities Hiring Machine Learning Researchs
Explore machine learning research openings in the cities hiring most right now.
See All 157+ Machine Learning Research Jobs
Find roles that match your experience and apply in just a few clicks.
Find JobsMachine Learning Research Job Market
Who's Hiring
- Meta23
- Scale AI17
- Microsoft7
- Achira5A
- Rad AI5
Top Industries Hiring
- Technology & Software20
- Artificial Intelligence11
- Electronics & Hardware6
- Investment & Asset Management6
- Biotechnology & Pharmaceuticals5
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 Meta, Scale AI, and Microsoft, with the largest share of openings in California, Washington, and New York, based on current listings on Migrate Mate as of November 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 41% of machine learning research openings are fully remote or hybrid as of November 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 157+ Machine Learning Research Jobs
Find roles that match your experience and apply in just a few clicks.
Find Jobs