Senior Machine Learning Engineer (Edge Deployment Specialist)

Posted 4 Days Ago
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London, Greater London, England
Hybrid
Senior level
Artificial Intelligence • Hardware • Information Technology • Security • Software • Cybersecurity • Big Data Analytics
We help people be their best in the moments that matter.
The Role
The Senior Machine Learning Engineer will deploy AI/ML models to edge devices, focusing on optimization and collaboration with cross-functional teams to improve model performance.
Summary Generated by Built In

Company Overview

At Motorola Solutions, we believe that everything starts with our people. We’re a global close-knit community, united by the relentless pursuit to help keep people safer everywhere. Our critical communications, video security and command center technologies support public safety agencies and enterprises alike, enabling the coordination that’s critical for safer communities, safer schools, safer hospitals and safer businesses. Connect with a career that matters, and help us build a safer future.


Department Overview
As a Senior Machine Learning Engineer.
Job Description

This role is primarily hybrid with weekly travel into our London offices around 2 days a week.

As a Senior Machine Learning Engineer, you will focus on deploying AI/ML models to a variety of edge devices, with particular expertise in converting models for Ambarella and Novatek SoCs. You will collaborate with cross-functional teams to ensure the efficient integration and optimization of models, ensuring they run seamlessly on low-power, resource-constrained devices.

Key Responsibilities

  • Convert, optimize, and deploy deep learning models for edge devices, focusing on Ambarella and Novatek SoCs.
  • Collaborate with data scientists, hardware engineers, and software developers to tailor models for resource-constrained environments.
  • Develop and implement model compression, quantization, and pruning techniques to improve performance.
  • Work closely with edge device firmware teams to ensure seamless integration of models and efficient execution on device hardware.
  • Contribute to the continuous improvement of machine learning deployment processes and tools within the organization.


Basic Requirements

  • Master's degree in a technical field.
  • 5+ years of professional experience in machine learning engineering, with a focus on deploying models to edge devices.
  • Strong understanding of model optimization techniques (quantization, pruning, knowledge distillation).
  • Experience in C++/Python programming and working with embedded systems.
  • Strong communication skills and ability to work collaboratively in a team-oriented environment.

Differentiators:

  • PhD in a technical field.
  • Experience with other edge AI platforms (e.g., Nvidia Jetson, OpenVino etc.).
  • Knowledge of cloud-edge integration strategies for model updates and performance monitoring.
  • Experience with Docker and Kubernetes.
  • Experience deploying and optimizing models for Ambarella and/or Novatek SoCs.
     

In return for your expertise, we’ll support you in this new challenge with coaching and development every step of the way. Also, to reward your hard work you’ll get:

  • Competitive salary and bonus schemes.
  • Two weeks additional pay per year (holiday bonus).
  • 25 days holiday entitlement + bank holidays. 
  • Attractive defined contribution pension scheme.
  • Employee stock purchase plan.
  • Life assurance.
  • Enhanced maternity and paternity pay.
  • Career development support and wide ranging learning opportunities.
  • Employee health and wellbeing support EAP, wellbeing guidance etc.
  • Carbon neutral initiatives/goals.
  • Corporate social responsibility initiatives including support for volunteering days.
  • Well known companies discount scheme.

#LI-KTB


Travel Requirements
Under 10%
Relocation Provided
None
Position Type
Experienced

Referral Payment PlanYes

CompanyMotorola Solutions UK Limited

EEO Statement

Motorola Solutions is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion or belief, sex, sexual orientation, gender identity, national origin, disability, veteran status or any other legally-protected characteristic. 

We are proud of our people-first and community-focused culture, empowering every Motorolan to be their most authentic self and to do their best work to deliver on the promise of a safer world. If you’d like to join our team but feel that you don’t quite meet all of the preferred skills, we’d still love to hear why you think you’d be a great addition to our team.

We’re committed to providing an inclusive and accessible recruiting experience for candidates with disabilities, or other physical or mental health conditions. To request an accommodation, please email [email protected].

Top Skills

C++
Docker
Kubernetes
Python

What the Team is Saying

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The Company
HQ: Chicago, IL
21,000 Employees
Hybrid Workplace
Year Founded: 1928

What We Do

About Motorola Solutions | Solving for safer

Safety and security are at the heart of everything we do at Motorola Solutions. We build and connect technologies to help protect people, property and places. Our technologies support public safety agencies and enterprises alike, enabling the collaboration that’s critical for safer communities, safer schools, safer hospitals and safer businesses.

Why Work With Us

We are a global family of driven, dynamic people who inspire and support everyone around us to be the best version of themselves. We embrace a “people first” philosophy – and are committed to creating and maintaining a culture of caring and inclusiveness. Are you ready to join our team and be a part of a close-knit community in a big company?

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Motorola Solutions Offices

Hybrid Workspace

Employees engage in a combination of remote and on-site work.

We believe that the next big idea can come from anyone, anywhere, at any time. That’s why we offer office-based, hybrid and remote working models, where Motorolans can do their best work wherever they work best.

Typical time on-site: Flexible
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