Machine Learning Engineer - Cloud

*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.

The team you will be part of

You will be joining our team focused on developing the Jabra Perform and BlueParrott product lines, aimed at improving tools for frontline workers.

As part of the team, you will design and deliver machine learning models and cloud-based systems that power and enhance frontline worker devices. You will train, fine-tune, and optimize models for production grade performance, contributing to scalable and reliable ML services. You will help build and maintain the team’s end-to-end workflows: Developing data pipelines, generating datasets, creating evaluation frameworks, and implementing backend services that support our devices and applications. In this role, you will collaborate across disciplines to shape new features and bring impactful products to market.

Your contribution is appreciated, and you will

• Design, build, and train scalable machine learning models and algorithms for cloud-based environments.
• Collect, preprocess, and analyze large and complex datasets to support model development and continuous refinement.
• Transition models from research to production, ensuring they are performant, maintainable, and ready to operate at scale.
• Deploy ML models into existing or newly developed backend services, integrating seamlessly with cloud infrastructure.
• Partner closely with engineers and product managers to deliver ML solutions that enhance and support our connected devices.
• Test, evaluate, and continuously monitor deployed models to ensure accuracy, robustness, and early detection of data drift or performance degradation.
• Build strong collaborative relationships with technical subject matterexperts, stakeholders, and leadership across the organization.
• Identify, communicate, and manage technical risks throughout the project lifecycle to ensure smooth delivery.

Sounds good so far? To thrive in this role, we envision that you bring

• 2+ years of experience in deploying and optimizing ML models in a production environment using cloud platforms like AWS, GCP, or Azure.
• Proficiency in ML frameworks (TensorFlow, PyTorch, Scikit-learn) with practical experience in designing and implementing ML models.
• Proficiency in programming languages such as Python, R, Java, or C++ for developing and optimizing machine learning algorithms.
• Familiarity with big data technologies and distributed computing frameworks for handling and processing large datasets.
• Fundamentals of audio and speech signal processing.

Pay Transparency Notice

• Depending on your work location, the target annual salary for this position can range from $86,000.00 to $135,000.00. In addition, you may be eligible for a discretionary bonus.
• Compensation for roles at GN depends on a wide array of factors including but not limited to location, role, skill set, and level of experience.
• To remain competitive, GN offers a competitive benefits package, including annual bonuses, health insurance, a 401(k) plan, paid time off and paid holidays.

We encourage you to apply

Even if you do not match all the above-mentioned skills, we will gladly receive your application if you think you have transferable skills. We greatly appreciate a mindset and motivation that aligns with our core values, helping both you and your team to thrive within the GN organization.

We are focused on an inclusive recruitment process

All applicants will receive equal consideration for employment.

Disability Accommodation

If you have a disability and you believe you need a reasonable accommodation in order to search for a job opening or to submit an online application, please e-mail . This email is provided for the purpose of supporting applicants who have a disability that prevents them from being able to apply online. Only emails received for this purpose will be returned. Emails left for other purposes, such as following up on an application or technical issues not related to a disability, will not receive a response.