Pay
$166K–$240K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 1,319 postings in the last year. Stated ranges, not salaries paid.
Research Engineer — Agentic Audio ML Workflows
US, MA - Framingham
Actively hiring, first seen by us 4 days ago- Pay
- Pay not listedSimilar roles state $166K–$240K
- Work setting
- Not stated
- Employment
Full-time — employment source
Employment type in the employer’s posting: Full time
From the employer’s posting
What you’ll work on
Full postingDesign agentic workflows that plan, launch, monitor and summarize audio ML experiments
Automate data prep, training, hyperparameter searches and regression testing
Build reliable tooling for on-prem GPU scheduling, tracking and reproducible runs
Connect LLM agents to scripts, datasets, evaluation suites and compute with safeguards
Turn repetitive research experiments into reusable workflows, dashboards and reports
From the employer’s posting
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.
What you’ll bring
All qualificationsCore experience
- Degree in computer science, machine learning, electrical engineering, or a related field, or equivalent practical experience.
Qualification wording
Degree in computer science, machine learning, electrical engineering, or a related field, or equivalent practical experience.
Posting record
Report an errorActively hiringFirst seen by us 4 days ago, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Nov 2, 20264 days ago
- Recorded sightings
- 4
- Last seen by us
- Nov 6, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Time since we first saw it
Day 4
Early. Similar jobs usually come down between day 6 and day 71.
At Boseallaboutme, jobs usually come down by about day 19.
From 4,619 similar postings that came down, and 9 of Boseallaboutme’s. Coming down is not always a hire.
Boseallaboutme is hiring
9 open jobs
Open jobs by the week we first saw them, last 12 weeks.
- ML Engineer hiring is picking up
- Takes jobs down quickly
See how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Python
- PyTorch
Where the posting names them
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.
· 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.
Your next step
Complete your application on boseallaboutme.wd503.myworkdayjobs.com. The employer’s form will show what is required.
Applying through Workday
- You create an account with each employer before you can upload anything. Every Workday employer has its own account, so one sign-up doesn’t carry over.
- “Autofill with Resume” comes right after sign-in, before the My Information and My Experience steps. Check every field it fills in those steps, since that’s what recruiters see.
- Your application status lives in that employer’s Candidate Home, under the same sign-in.
Already applied? Track this application
Demand for ML Engineer roles
New ML Engineer postings at the employers we follow are up 12% over the last 5 weeks compared with the 5 before.
Each bar is one week of new postings. The darkest bars are the last 5 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
About the role
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
US government figures for computer and information research scientists
- Median yearly pay, May 2025
- $140,300
- Expected change, 2025 to 2035
- 21.8% more jobsagainst 3.5% for all jobs
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
Job 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 DescriptionDescription
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.
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
No pay amount identified in the saved description.
- Location & working pattern
US, MA - Framingham
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.