Machine Learning Engineer

Proximie Limited
London
10 months ago
Applications closed

Related Jobs

View all jobs

Machine Learning Engineer

Machine Learning Engineer / MLOps Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Five billion people in the world do not have access to safe and affordable surgery, and this is a problem everywhere, not just in developing countries. Proximie is on a mission to improve healthcare by transforming the world’s operating rooms into connected ecosystems of people, devices, and data.

Proximie’s advanced telepresence and cloud data solutions ensure seamless information flow in and out of the operating room (OR). Once collected, this data becomes a “single source of truth,” allowing healthcare professionals to utilize powerful analytics to identify patterns, trends, and bottlenecks, as well as uncover optimization opportunities.

Proximie empowers medical device companies and healthcare organizations with operating room intelligence to drive productivity and innovation.

Our key solution areas are:

  • Real-time Connectivity:Be in the operating room from anywhere.
  • Unified Data:Creating a single source of truth in the OR.
  • Analytics and Insight:Turning incisive insight into decisive results.
  • Video Library:Learn more from every procedure.

Proximie was Founded in 2016 by Dr. Nadine Hachach Haram, an NHS surgeon and is headquartered in London with offices in the United States and Lebanon. Proximie has 100 employees today and is deployed in over 50 countries.

Position Overview

As Proximie continues to turn every activity and event in the operating room into comprehensive, structured, and context-rich data that drive better insights and decision making we are seeking two machine learning engineers to join our data team. Using your deep technical expertise and real-world experience you will be instrumental in developing and deploying machine learning solutions that improve clinical outcomes, drive productivity and support Proximie’s continuing evolution into the unrivalled champion of the intelligent operating room.

The first machine learning engineer position will work with multi-modal data (audio, vision, and language) to curate, consolidate, and augment retrospective and prospective datasets that fuel the development of advanced machine learning solutions. You’ll harness the power of Proximie’s deep data lakes and apply cutting-edge generative techniques to solve real-world challenges for Proximie’s customers.

The second machine learning engineer position will lead the development of intelligent systems that can automatically detect and capture key events in the operating room. You’ll design robust solutions that work across diverse and unpredictable data distributions, tackling challenges like identifying rare events buried in hours of surgical video.

Both roles demand creativity, precision, and a deep understanding of real-world machine learning at scale. If you're excited by complex problems with life-changing impact and want to build tech that operates in the most critical environments, we’d love to hear from you.

Responsibilities

  • Collaborate with product, engineering and commercial teams to develop and deploy AI models for real world application in hospitals all over the world.
  • Design, train and validate machine learning mono and multi-modal models using state of the art approaches.
  • Develop models and derived tools robust to the heterogeneity of operating room environments. With customers all over the world operating rooms are often different which creates unique opportunities for problem solving and model design.
  • Own the full model lifecycle including but not limited to data curation, model implementation, training, validation, deployment, and maintenance.
  • Development within Proximie environment to enable dynamic model training and performance evaluation while integrating with Proximie’s data lakes.
  • Document solutions and contribute to internal knowledge sharing and capability building.

Requirements

  • PhD in a machine learning field such as computer science, data science, engineering, or a related field. Masters considered but PhD preferred.
  • Minimum of 4 years’ hands-on experience in industry, developing and deploying AI solutions which solve real-world problems.
  • Expertise in developing, training and fine-tuning machine learning and multi-modal models. Experience in training models with data originating from heterogeneous distributions is highly desirable.
  • Deep knowledge of a variety of traditional machine learning, deep learning and generative AI methods for both supervised, self-supervised and unsupervised learning with an emphasis on vision.
  • Proficiency with deep learning frameworks such as TensorFlow/PyTorch.
  • Proficiency with Python and strong software development background.
  • Experience with MLOps practices, including versioning, deployment, and monitoring of models highly desirable.
  • Ability to communicate complex technical concepts clearly to non-technical stakeholders.

Why Work for Proximie?

  • You will be encouraged to grow in your role, take ownership and gain responsibilities. Proximie’s values are Ownership, Deliver Results, Build Trust and Go Beyond.
  • Generous annual leave.
  • Two “well-being” days per year plus the day off for your birthday.
  • “Summer Fridays” – early office closing on Fridays during summer months.
  • Annual bonus programme – based on individual contribution.
  • To support your professional growth, all permanent employees will have access to an annual stipend of £1,000 to assist with personal development activities.
  • Flexible working hours - we trust our people to manage their time and to focus on wider results.
  • A flat organizational structure where every opinion matters, ideas are cultivated, and innovation is encouraged.
  • Proximie is a truly global company with teams across the UK, Europe, United States and the Middle East with that you will have opportunities to see the world.

Proximie is an equal-opportunity employer. We are committed to providing a work environment that supports, inspires, and respects all individuals. We do not discriminate on the basis of race, colour, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, veteran status, or any other status protected under the law.

#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

What Hiring Managers Look for First in AI Job Applications (UK Guide)

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.