Machine Learning Engineer - Efficient Machine Learning

Bose - Framingham, MA

Full-time

Framingham, MA

Furniture, Home Furnishings, Electronics, and Appliance Retailers

About the position

Bose Research is searching for an outstanding machine learning engineer specializing in efficient deep learning to join our research team. The ideal candidate will possess several years of experience in crafting deep learning solutions and a proven track record in the field. A strong understanding of software development and engineering principles is essential. We are looking for someone who is passionate about pushing the boundaries of what can be achieved with AI and has a deep understanding of computer science fundamentals. The role will involve working on various aspects of deep learning, particularly in relation to audio-related problems, and will require a background in techniques such as pruning, quantization, neural architecture search (NAS), and knowledge distillation. In this position, you will be responsible for designing, implementing, deploying, and optimizing deep learning models, data structures, and algorithms specifically for resource-constrained devices. You will research, design, and implement novel methods for efficient deep learning, focusing on audio-related applications. This includes training and evaluating deep learning models to enable new experiences on our headphones, earbuds, soundbars, portable speakers, and other consumer audio devices. Additionally, you will evaluate neural network hardware accelerators for low-power wearable devices, collaborating with various vendors' custom toolchains, software, and hardware, while informing leadership teams about trade-offs involved in these technologies. You will also work closely with product engineering groups to transfer technology and mentor interns or co-ops, fostering a collaborative and innovative environment.

Responsibilities

• Design, implement, deploy and optimize deep learning models, data structures, 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 trade-offs.
• Work with product engineering groups to transfer technology.
• Mentor interns/co-ops.

Requirements

• A master's degree or Ph.D. in Computer Science, Electrical Engineering, or a related field, with a focus on machine learning, artificial intelligence, or a closely related discipline.
• Background in efficient ML techniques such as quantization, pruning, neural architecture search, knowledge distillation, and efficient model architecture.
• Familiarity with deep learning frameworks such as TensorFlow, PyTorch, or similar.
• Strong programming skills in Python, C/C++, and other programming languages.
• Excellent communication skills.

Nice-to-haves

• Hands-on experience with building software applications and systems (e.g., embedded, desktop, cloud-based applications).
• Strong publication record in top-tier ML/AI conferences and journals.
• Strong curiosity about hardware architecture, model development, and software systems.
• Experience working on digital signal processing, audio deep learning, or multi-modal models that involve audio.