Machine Learning Engineer - Edge

GN Group•Lowell, NH

1d•Hybrid

About The Position

Machine Learning Engineer - Edge Please consider before applying: This is a hybrid role, and candidates must reside within a commutable distance of one of our offices in either Dover, NH, or Lowell, MA. Turn up the volume on your career as Cloud AI/ML Engineer GN brings people closer through our advanced intelligent hearing, audio, video, and gaming solutions. Inspired by people and driven by innovation, we deliver technology that enhances the senses of hearing and sight. We help people with hearing loss overcome real-life challenges, improve communication and collaboration for businesses, and provide great experiences for audio and gaming enthusiasts. The team you will be part of You will be joining our team focused on developing the Jabra Perform and BlueParrott product lines to advance solutions for frontline workers. As part of the team, you will design and deploy on device machine learning models that power speech enhancement, augmented hearing, and real-time environmental awareness. You will train, fine‑tune, and compress models to run efficiently on resource-constrained edge hardware without compromising accuracy or performance. You will bridge the gap between software and hardware; collaborating closely with hardware engineers to ensure AI models are well integrated into the device architecture. Your work will also include optimizing algorithms for low power environments and maintaining the software libraries, tools, and frameworks that enable modern edge AI development.

Requirements

2+ years of experience developing machine learning solutions, including work in audio and speech processing.
Proficient in programming with C, C++, and Python.
Experience with machine learning frameworks such as TensorFlow Lite, PyTorch, or comparable edge-focused toolchains.
Solid theoretical and practical understanding of ML architecture design, training, evaluation, and deployment workflows.
Understanding of model compression, quantization, and other optimization techniques for resource constrained edge devices.
Experience with embedded systems and hardware platforms.
Fundamentals of audio and speech signal processing.

Nice To Haves

transferable skills

Responsibilities

Develop and optimize AI/ML models specifically designed for resource‑constrained edge devices.
Collect, preprocess, and analyze large and complex datasets to train, fine‑tune, and validate models.
Apply techniques such as model compression, quantization, pruning, and distillation to improve efficiency and runtime performance.
Ensure models perform reliably in real‑world environments, meeting both functional and non‑functional requirements.
Collaborate closely with hardware engineers and other cross‑functional teams to align AI/ML solutions with device architecture and system constraints.
Develop effective working relationships with technical experts, stakeholders, and leaders across the organization.
Identify, communicate, and manage technical risks throughout the project lifecycle.