Edge AI ML Engineer

Bose Corporation•Framingham, MA

1d•$141,000 - $193,950•Onsite

About The Position

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. About the Team – Reference Software & ML Algorithms Group, System Engineering, Architecture & Platforms We are building the algorithms, systems, and platforms that power the next generation of audio and multimodal experiences—from embedded devices to cloud‑connected products. Our ambition is bold: to become the world’s Audio Lab — inventing, optimizing, and delivering meaningful and magical experiences anywhere sound matters. We sit at the intersection of digital signal processing (DSP), deep learning, embedded systems, and software infrastructure, turning new ideas into real‑world capabilities across a wide range of hardware and product surfaces. Our group: Invents and develops audio and multimodal ML algorithms for real-world product experiences. Designs efficient neural networks and signal processing pipelines for embedded and on-device deployment. Optimizes models for resource-constrained hardware, including MCUs, DSPs, NPUs, and custom accelerators. Build tools, infrastructure, and reference implementations that accelerate the journey from invention  prototype  product. Learns from real-world deployment constraints to inform the next generation of algorithms, models, and platforms. If you love building ML systems where algorithms meet real hardware –– and you want your models to run efficiently in the real world –– this is the place. About the Role As an Edge AI ML Engineer, you will develop, optimize, and deploy machine learning models for audio and multimodal intelligence on real devices. You will work at the boundary of ML modeling, DSP, and embedded systems, turning research concepts into efficient, robust, production-ready algorithms. You will collaborate closely with ML researchers, DSP experts, firmware engineers, and hardware teams to design algorithms that perform well not only in the lab, but also under real-world constraints such as latency, memory and power. This role is ideal for an ML engineer who enjoys model development, experimentation, and algorithm design, while also caring deeply about whether those models can run efficiently on edge hardware. In This Role, You Will ML Modeling & Algorithm Development Identify opportunities for new DSP/ML algorithms by deeply understanding device constraints, sensor characteristics, and hardware capabilities (MCU, DSP, NPU). Develop audio and multimodal ML models for embedded and edge AI applications. Design, train, evaluate, and iterate on models for real-world sensing and interaction use cases. Prototype novel approaches that push what’s possible in low‑latency, on‑device audio and multimodal processing. Embedded / On‑Device Engineering Develop software for RTOS environments (e.g., FreeRTOS) and deploy models to device runtimes and hardware accelerators (DSP, NPU, MCU). Convert trained ML models into efficient embedded implementations (C/C++, quantization, fixed‑point inference). Optimize runtime performance: memory footprint, SRAM usage, latency, and power consumption. Integrate ML inference into real‑time firmware pipelines. Design end‑to‑end embedded AI systems (sensor → preprocessing → model → post‑processing). Platform & Performance Engineering Architect reusable embedded ML platform components usable across multiple hardware targets. Profile and optimize performance on MCUs, DSP cores, NPUs, and custom accelerators. Work with cross‑compilation toolchains, CMake‑based builds, and modular codebases. Integrate with on‑device ML runtimes (e.g., TFLite Micro, ExecuTorch, custom interpreters). Provide actionable feedback on model architecture for deployment efficiency and real‑time behavior. Required Qualifications Strong proficiency in C/C++ for embedded systems. Strong experience developing and evaluating machine learning models, preferably for audio, speech, or other time-series sensor data. Experience optimizing ML models for edge, embedded, or resource-constrained environments. Proficiency in Python for model development, experimentation, evaluation, and tooling. Preferred Qualifications Master’s or Ph.D. in Computer Science, Electrical Engineering, Machine Learning, or related field. Experience with ML compilers/frameworks such as MLIR, Glow, ExecuTorch. Experience with real-time streaming inference pipelines. Knowledge of acoustics and classical audio DSP. Experience with on-device ML (TinyML, quantization, pruning). Publication track record in ML, DSP, systems, or embedded AI. You Might Thrive Here If You… Love building systems where algorithms meet real hardware. Enjoy profiling and optimizing code under tight compute and memory constraints. Take ownership end‑to‑end — from prototype to hardware bring‑up to production. Thrive in fast‑moving, ambiguous, zero‑to‑one environments. Want your work to directly shape the next generation of audio‑driven experiences. 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.

Requirements

Strong proficiency in C/C++ for embedded systems.
Strong experience developing and evaluating machine learning models, preferably for audio, speech, or other time-series sensor data.
Experience optimizing ML models for edge, embedded, or resource-constrained environments.
Proficiency in Python for model development, experimentation, evaluation, and tooling.

Nice To Haves

Master’s or Ph.D. in Computer Science, Electrical Engineering, Machine Learning, or related field.
Experience with ML compilers/frameworks such as MLIR, Glow, ExecuTorch.
Experience with real-time streaming inference pipelines.
Knowledge of acoustics and classical audio DSP.
Experience with on-device ML (TinyML, quantization, pruning).
Publication track record in ML, DSP, systems, or embedded AI.

Responsibilities

Identify opportunities for new DSP/ML algorithms by deeply understanding device constraints, sensor characteristics, and hardware capabilities (MCU, DSP, NPU).
Develop audio and multimodal ML models for embedded and edge AI applications.
Design, train, evaluate, and iterate on models for real-world sensing and interaction use cases.
Prototype novel approaches that push what’s possible in low‑latency, on‑device audio and multimodal processing.
Develop software for RTOS environments (e.g., FreeRTOS) and deploy models to device runtimes and hardware accelerators (DSP, NPU, MCU).
Convert trained ML models into efficient embedded implementations (C/C++, quantization, fixed‑point inference).
Optimize runtime performance: memory footprint, SRAM usage, latency, and power consumption.
Integrate ML inference into real‑time firmware pipelines.
Design end‑to‑end embedded AI systems (sensor → preprocessing → model → post‑processing).
Architect reusable embedded ML platform components usable across multiple hardware targets.
Profile and optimize performance on MCUs, DSP cores, NPUs, and custom accelerators.
Work with cross‑compilation toolchains, CMake‑based builds, and modular codebases.
Integrate with on‑device ML runtimes (e.g., TFLite Micro, ExecuTorch, custom interpreters).
Provide actionable feedback on model architecture for deployment efficiency and real‑time behavior.