ML Research Engineer Jobs
ML Research Engineer jobs are open across technology, healthcare, finance, and defense, from new-grad to staff and principal level, with specializations in foundation models, reinforcement learning, and computer vision. Find a role that fits from the openings below and apply directly.
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Showing 5 of 144+ ML Research Engineer 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.
ML Research Engineer Jobs by Experience Level
Top Cities Hiring ML Research Engineers
Explore ML research engineer openings in the cities hiring most right now.
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Find ML Research Engineer JobsML Research Engineer Job Market
Who's Hiring
- Meta23
- Scale AI17
- Microsoft6
- Achira5A
- Rad AI5
Top Industries Hiring
- Technology & Software18
- Artificial Intelligence11
- Electronics & Hardware5
- Biotechnology & Pharmaceuticals5
- Banking & Financial Services4
What Employers Look For
The qualifications that appear most often in ML research engineer jobs.
- Advanced degree in machine learning, computer science, or a closely related field
- Proficiency in Python and deep learning frameworks such as PyTorch or JAX
- Experience designing, training, and evaluating large-scale neural network models
- Publication record or demonstrated original research contributions in ML or AI
- Strong mathematical foundation in statistics, linear algebra, and optimization
- Experience with distributed training, cloud compute platforms, or MLOps tooling
Tips for Your ML Research Engineer Job Search
Tailor your resume to the research stack
List the specific frameworks you've used in production or published research, PyTorch, JAX, or TensorFlow, alongside your model architectures. Hiring managers scan for these before reading your experience bullets, so front-load them in a skills or technical summary section.
Link publications and open-source contributions
Attach a Google Scholar profile or GitHub repository to every application. ML research teams weigh published papers and reproducible code heavily, and a single first-author paper at a top venue can move you past the resume screen faster than years of industry experience alone.
Apply early to roles that fit
Migrate Mate lists ml research engineer 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 blur the line between pure research and applied engineering. Read each listing for keywords like 'publish,' 'novel methods,' or 'production serving.' Targeting roles that match your actual focus, foundational versus deployment, improves your interview fit and reduces mismatched offers.
Prepare a research talk, not just a portfolio
Most ML research engineer loops include a technical presentation on your own work. Practice explaining your problem setup, baselines, ablations, and results in 20 minutes. Teams evaluate how you reason about failure modes, not just whether your final numbers were strong.
Negotiate compute resources alongside compensation
GPU cluster access, cloud compute budgets, and dataset licensing rights affect your ability to do the job. Raise these in the offer stage alongside salary. Teams that can't answer concretely often signal a mismatch between the research mandate and actual infrastructure investment.
ML Research Engineer Jobs: Frequently Asked Questions
Which companies are hiring the most ml research engineers?
The companies hiring the most ml research engineers 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 and well-funded AI labs, though defense contractors and healthcare AI startups have expanded hiring meaningfully in recent cycles.
How many ml research engineer jobs are remote?
About 39% of ml research engineer openings are fully remote or hybrid as of November 2026, reflecting strong demand from teams that operate across distributed research centers. Roles focused on language modeling and data-centric AI tend to offer the most remote flexibility, while positions requiring access to proprietary hardware clusters or on-site collaboration with product teams are more likely to require in-person presence.
How do you become a ml research engineer?
Start by building a strong foundation in mathematics, particularly linear algebra, probability, and calculus, alongside proficiency in Python and a major deep learning framework. Complete graduate-level coursework or a master's or doctoral program in machine learning or computer science. Contribute to open-source projects, replicate published papers, and aim to publish or present original work. Internships at research labs or AI teams convert directly into full-time roles and are often the most direct path in.
Can you get hired as a ml research engineer without much experience?
Yes, though the bar is high without a graduate degree or publications. The most effective entry points are research internships during a master's or doctoral program, open-source contributions to widely used ML libraries, and strong performance in research-adjacent roles such as data scientist or ML engineer. Replicating landmark papers with novel extensions and sharing results publicly demonstrates research capability in the absence of a formal publication record.
What does the ml research engineer interview process look like?
Most loops include a recruiter screen, a coding round focused on data structures and ML fundamentals, a research discussion where you walk through a past project in depth, and a technical presentation on original work. Some teams add a take-home research problem or a whiteboard session on model design and experimental methodology. Final rounds typically involve senior researchers evaluating your ability to scope a research problem and reason clearly about tradeoffs.
Where can I find and apply to ml research engineer jobs?
You can find and apply to ml research engineer 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 focus area and experience level, then apply directly to each listing that fits.
See All 144+ ML Research Engineer Jobs
Find roles that match your experience and apply in just a few clicks.
Find ML Research Engineer Jobs