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About This Role
Location: Framingham, Massachusetts, United States of America Category: Engineering Job ID: R29312 Brand: Bose Corporation, U.S.A Posted Date: October 20th 2026 widget: Full time
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
About the Role
Bose is seeking a Machine Learning Engineer with expertise in Efficient AI and Edge ML to join our team. In this role, you will design, train, optimize, and deploy machine learning models for next-generation audio products. You will work closely with embedded, DSP, compiler, hardware, and software engineers to bring AI capabilities to resource-constrained devices.
The ideal candidate has strong machine learning fundamentals, practical experience with efficient model development, and an understanding of Edge ML deployment constraints. Deep embedded systems expertise is not required. However, you should be able to understand deployment and integration feedback, identify model-related limitations, and quickly iterate on model architectures and training methods to improve accuracy, latency, memory usage, and power efficiency.
Responsibilities
- Design, train, evaluate, and optimize machine learning models for consumer audio products.
- Apply Efficient ML techniques, including quantization, pruning, knowledge distillation, model compression, and efficient model architectures, to improve model efficiency while maintaining model quality.
- Work closely with embedded, DSP, compiler, and hardware engineers to deploy and integrate machine learning models on resource-constrained platforms, including DSPs, NPUs, microcontrollers, and ARM-based systems.
- Analyze deployment and integration feedback, identify model-related issues, and rapidly iterate on model architectures and training methodologies.
- Adapt models to address platform constraints such as unsupported operators, data-type limitations, memory usage, latency, compute requirements, and power consumption.
- Profile and evaluate model performance across accuracy, latency, memory footprint, model size, compute efficiency, and power usage.
- Develop and maintain repeatable workflows for model training, conversion, compilation, simulation, validation, and performance analysis.
- Collaborate across Machine Learning, embedded software, DSP, hardware, compiler, and product engineering teams to deliver production-ready AI solutions.
- Evaluate emerging ML technologies and AI-assisted engineering tools that can improve model development, deployment, debugging, and optimization workflows.
- Document experiments, technical findings, deployment learnings, and reusable engineering practices.
- Mentor interns and co-op engineers.
Minimum Qualifications
- M.S. or Ph.D. in Computer Science, Electrical Engineering, Computer Engineering, Machine Learning, or a related field.
- Strong foundation in machine learning and deep learning, including model architecture design, training, evaluation, and optimization.
- Working knowledge of Efficient ML techniques such as quantization, pruning, knowledge distillation, model compression, and efficient model architectures.
- Understanding of how model optimization techniques affect model accuracy, latency, memory footprint, compute requirements, and deployment compatibility.
- Proficiency with deep learning frameworks such as PyTorch, TensorFlow, or similar.
- Strong Python programming, debugging, and problem-solving skills.
- Working knowledge of C/C++ and the ability to collaborate effectively with engineers working on embedded and production systems.
- Understanding of Edge ML concepts and common resource constraints, including computation, memory, latency, and power.
- Ability to analyze model behavior and troubleshoot issues across training, conversion, deployment, and integration workflows.
- Strong communication and cross-functional collaboration skills.
Preferred Qualifications
- Hands-on experience with quantization-aware training, post-training quantization, mixed-precision inference, structured or unstructured pruning, operator replacement, or hardware-aware model optimization.
- Experience adapting model architectures or training workflows in response to deployment and integration constraints.
- Experience deploying machine learning models on edge devices, DSPs, NPUs, mobile platforms, wearable devices, or other resource-constrained systems.
- Familiarity with deployment frameworks and toolchains such as TensorFlow Lite, TensorFlow Lite Micro, ONNX Runtime, TVM, MLIR, or vendor-specific SDKs.
- Understanding of deployment and performance metrics such as latency, memory footprint, model size, MACs, memory bandwidth, and power consumption.
- Experience with audio ML, speech processing, acoustic sensing, or multimodal models involving audio.
- Familiarity with LLM- or agent-based engineering workflows, including knowledge retrieval, tool calling, technical documentation analysis, structured data extraction, failure diagnosis, benchmark analysis, or optimization recommendations.
- Experience building or evaluating AI-assisted tools that improve ML development, deployment, debugging, or engineering productivity.
- Curiosity about the interaction between machine learning models, software toolchains, and hardware platforms.
#LI-Hybrid
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: $141,000-$193,950.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.
Salary Context
This $141K-$193K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Bose, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($167K) sits 21% below the category median. Disclosed range: $141K to $193K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Bose AI Hiring
Bose has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in Framingham, MA, US. Compensation range: $64K - $193K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (140) are outnumbered by mid-level (5,630) and senior (4,176) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $266,050 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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