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.
Embedded Audio ML Engineer Co-op
US, MA - Framingham
Live, first seen by us 23 days ago- Pay
$40–51.25/hourLocation-specific pay — pay source
Solid background in audio deep learning and digital signal processing At Bose, you're inspired to be and do your best and are rewarded for your unique talents! Our compensation is thoughtfully tailored to your skills, experience, education, and location, and goes beyond base salary. The hiring range for this position in the primary work location of Framingham, Massachusetts is: $40.00-$51.25 per hour.The hiring range for other Bose work locations may vary.

In addition to competitive base pay we offer rewards including bonus programs, comprehensive health and welfare benefits, a 401(k) plan, plus exclusive perks designed to support your wellbeing, and a generous employee discount where you can immerse yourself in our products and experiences. We are a proudly independent company—driven by purpose, guided by our values, and united by a belief in the power of sound. As the world leader in audio experiences, we’re creating what’s next—pushing boundaries and delivering transformative sound experiences for people everywhere. Join us and make your next career move a mic-drop. Let’s Make Waves.
Read the full posting- Work setting
- Not stated
- Employment
Full-time — employment source
Employment type in the employer’s posting: Full time
From the employer’s posting
Before you apply
- New graduates
- For current students
Job title: Embedded Audio ML Engineer Co-op
What you’ll work on
Full postingBuild embedded software prototypes of ML-powered audio features for wearables
Design, deploy and optimize deep learning models on resource-constrained devices
Research efficient methods such as quantization, pruning, and knowledge distillation for audio
Train and evaluate deep learning models for headphones, earbuds, and speakers
Evaluate neural network hardware accelerators for low-power wearables and report tradeoffs
From the employer’s posting
Build embedded software prototypes to demonstrate new ML-powered audio features on our wearables
Design, implement, deploy and optimize deep learning models, data structure and algorithms on resource-constrained devices.
Research, design and implement novel methods for efficient deep learning (e.g., quantization, pruning, NAS, knowledge distillation) with a focus on audio-related problems.
Train and evaluate deep learning models to enable new experiences on our headphones, earbuds, soundbars, portable speakers and other consumer audio devices.
Evaluate neural network hardware accelerators for low-power wearable devices, including working with various vendors’ custom toolchains, software and hardware and informing leadership teams about tradeoffs.
What you’ll bring
All qualificationsCore experience
- Strong proficiency in C/C++ for embedded systems.
- Proficiency in Python for model conversion, evaluation, or tooling workflows.
- Strong experience implementing deep neural networks with PyTorch, TensorFlow, or similar frameworks
Preferred experience
- Experience deploying ML models on edge or resource‑constrained devices (MCU/DSP/NPU).
Qualification wording
Strong proficiency in C/C++ for embedded systems.
Proficiency in Python for model conversion, evaluation, or tooling workflows.
Strong experience implementing deep neural networks with PyTorch, TensorFlow, or similar frameworks
Experience deploying ML models on edge or resource‑constrained devices (MCU/DSP/NPU).
Education & alternatives
Minimum Qualifications - Currently pursuing a M.S. or Ph.D. in Computer Science, Electrical Engineering, Machine Learning, or a related field - Strong proficiency in C/C++ for embedded systems.
About Boseallaboutme
Bose Corporation is seeking students to participate in our Co-op Program who share our belief that sound is power.
In the employer’s words · Read in context
Posting record
Report an error- Status in our records
- Live
- First seen by us
- Oct 13, 202623 days ago
- Recorded sightings
- 30
- 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 23
Similar jobs usually come down between day 6 and day 70.
At Boseallaboutme, jobs usually come down by about day 19.
From 4,599 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.
- C
- Python
- PyTorch
- TensorFlow
- C++
Where the posting names them
- Currently pursuing a M.S. or Ph.D. in Computer Science, Electrical Engineering, Machine Learning, or a related field - Strong proficiency in C/C++ for embedded systems. - Proficiency in Python for model conversion, evaluation, or tooling workflows.
- Strong proficiency in C/C++ for embedded systems. - Proficiency in Python for model conversion, evaluation, or tooling workflows. - Strong experience implementing deep neural networks with PyTorch, TensorFlow, or similar frameworks
- Proficiency in Python for model conversion, evaluation, or tooling workflows. - Strong experience implementing deep neural networks with PyTorch, TensorFlow, or similar frameworks - Proven cross-group and cross-culture collaboration skills
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 DescriptionABOUT THE CO-OP PROGRAM
Bose Corporation is seeking students to participate in our Co-op Program who share our belief that sound is power. During the six-month co-op, students will have the chance to apply classroom learning in a hands-on work environment. Co-op participants will network across the business to gain diverse perspectives within Bose Corporation. Additionally, co-ops will connect with other students and colleagues, building valuable professional relationships for the future.
Spring Co-op: Candidates must be available to work full-time (40 hours per week) in a hybrid, in-office format from January through July, 2027. No relocation assistance is available.
THE ROLE
At Bose, we use machine learning across a diverse set of problems ranging from speech enhancement (e.g., SpeechClarity) to audio perception. As an Embedded Audio ML Engineer Co-op at Bose, you will help deploy and optimize our next generation deep learning algorithms for resource-constrained devices.
Responsibilities
- Build embedded software prototypes to demonstrate new ML-powered audio features on our wearables
- Design, implement, deploy and optimize deep learning models, data structure and algorithms on resource-constrained devices.
- Research, design and implement novel methods for efficient deep learning (e.g., quantization, pruning, NAS, knowledge distillation) with a focus on audio-related problems.
- Train and evaluate deep learning models to enable new experiences on our headphones, earbuds, soundbars, portable speakers and other consumer audio devices.
- Evaluate neural network hardware accelerators for low-power wearable devices, including working with various vendors’ custom toolchains, software and hardware and informing leadership teams about tradeoffs.
Minimum Qualifications
- Currently pursuing a M.S. or Ph.D. in Computer Science, Electrical Engineering, Machine Learning, or a related field
- Strong proficiency in C/C++ for embedded systems.
- Proficiency in Python for model conversion, evaluation, or tooling workflows.
- Strong experience implementing deep neural networks with PyTorch, TensorFlow, or similar frameworks
- Proven cross-group and cross-culture collaboration skills
- Creative problem-solver with demonstrated ability to work independently
Preferred Qualifications
- Prior internship or work experience as a Software/ML engineer
- Experience deploying ML models on edge or resource‑constrained devices (MCU/DSP/NPU).
- Solid background in audio deep learning and digital signal processing
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
Solid background in audio deep learning and digital signal processing At Bose, you're inspired to be and do your best and are rewarded for your unique talents! Our compensation is thoughtfully tailored to your skills, experience, education, and location, and goes beyond base salary. The hiring range for this position in the primary work location of Framingham, Massachusetts is: $40.00-$51.25 per hour.The hiring range for other Bose work locations may vary.

In addition to competitive base pay we offer rewards including bonus programs, comprehensive health and welfare benefits, a 401(k) plan, plus exclusive perks designed to support your wellbeing, and a generous employee discount where you can immerse yourself in our products and experiences. We are a proudly independent company—driven by purpose, guided by our values, and united by a belief in the power of sound. As the world leader in audio experiences, we’re creating what’s next—pushing boundaries and delivering transformative sound experiences for people everywhere. Join us and make your next career move a mic-drop. Let’s Make Waves.
- Location & working pattern
US, MA - Framingham
Bose Corporation is seeking students to participate in our Co-op Program who share our belief that sound is power. During the six-month co-op, students will have the chance to apply classroom learning in a hands-on work environment. Co-op participants will network across the business to gain diverse perspectives within Bose Corporation. Additionally, co-ops will connect with other students and colleagues, building valuable professional relationships for the future. Spring Co-op: Candidates must be available to work full-time (40 hours per week) in a hybrid, in-office format from January through July, 2027. No relocation assistance is available. THE ROLE
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.