Software Engineer - Medical Device

CT19
Oxford
2 weeks ago
Applications closed

Related Jobs

View all jobs

Software Engineer

Software Engineer

Software Engineer

Software Engineer

Software Engineer Lead

Software Engineer - Mandarin Speaking

We are recruiting for an early-stage start-up based in Oxford who have recently secured their Seed funding. They are building a platform that leverages extensive Oxford University research in Raman Spectroscopy / Machine Learning to bring a disruptive technology allowing culture-free microbiology diagnostics in hours. The technology can identify bacteria and fungi from clinical samples and rapidly produce their antimicrobial profile in under 3 hours, compared to the current standard of 3 days. The success of this technology will ensure a revolution in the microbiology diagnostic space and set the standard for tomorrow.


This is a rare opportunity to join as one of the first employees on a mission to win against superbugs, we need the best and brightest to join us and enable this ambitious vision.


As an experienced Medical Device Software Engineer, you will take a hands-on role leading the development of the software for their initial prototype.


Job Title: Software Engineer – Medical Devices

Location: Oxford (Hybrid)

Salary: Highly Negotiable and Dependant on Experience


We are looking for a skilledFull-Stack Developerwith expertise inPythonto work on both front-end and back-end development of an innovative medical device. The ideal candidate will have experience working with medical devices in Python, and it would be a big bonus if they have worked with spectroscopy / imaging data.


Key Responsibilities:

  • Develop and maintain Software for both Back-End and Front-End.
  • Build and optimize APIs and backend services.
  • Integrate existing SDKs for hardware control / data acquisition from Raman spectroscopy systems.
  • Work with databases, ensuring efficient data storage and retrieval.
  • Collaborate with cross-functional teams to define and deliver new features.
  • Ensure high performance, security, and scalability of applications.
  • Testing and Debugging including unit / integration tests.
  • Collaborate with quality assurance teams to document processes in line with ISO13485.


Requirements:

  • Proficiency inPythonand relevant frameworks
  • Experience with frontend technologies (e.g., HTML, CSS, JavaScript, React).
  • Familiarity with cloud services (AWS, Azure, or GCP) is a plus.
  • Knowledge of version control systems like Git.
  • Familiarity with communication protocols such as USB, Serial, or Ethernet.
  • Ability to work in an agile environment and problem-solve effectively.


Please apply with an up to date CV for consideration.

Get the latest insights and jobs direct. Sign up for our newsletter.

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

Industry Insights

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

Tips for Staying Inspired: How Machine Learning Pros Fuel Creativity and Innovation

Machine learning (ML) continues to reshape industries—from personalised e-commerce recommendations and autonomous vehicles to advanced healthcare diagnostics and predictive maintenance in manufacturing. Yet behind every revolutionary model lies a challenging and sometimes repetitive process: data cleaning, hyperparameter tuning, infrastructure management, stakeholder communications, and constant performance monitoring. It’s no wonder many ML professionals can experience creative fatigue or get stuck in the daily grind. So, how do machine learning experts keep their spark alive and continually generate fresh ideas? Below, you’ll find ten actionable strategies that successful ML engineers, data scientists, and research scientists use to stay innovative and push boundaries. Whether you’re an experienced practitioner or just breaking into the field, these tips can help you fuel creativity and discover new angles for solving complex problems.

Top 10 Machine Learning Career Myths Debunked: Key Facts for Aspiring Professionals

Machine learning (ML) has become one of the hottest fields in technology—touching everything from recommendation engines and self-driving cars to language translation and healthcare diagnostics. The immense potential of ML, combined with attractive compensation packages and high-profile success stories, has spurred countless professionals and students to explore this career path. Yet, despite the boom in demand and innovation, machine learning is not exempt from myths and misconceptions. At MachineLearningJobs.co.uk, we’ve had front-row seats to the real-life career journeys and hiring needs in this field. We see, time and again, that outdated assumptions—like needing a PhD from a top university or that ML is purely about deep neural networks—can mislead new entrants and even deter seasoned professionals from making a successful transition. If you’re curious about a career in machine learning or looking to take your existing ML expertise to the next level, this article is for you. Below, we debunk 10 of the most persistent myths about machine learning careers and offer a clear-eyed view of the essential skills, opportunities, and realistic paths forward. By the end, you’ll be better equipped to make informed decisions about your future in this dynamic and rewarding domain.

Global vs. Local: Comparing the UK Machine Learning Job Market to International Landscapes

How to evaluate opportunities, salaries, and work culture in machine learning across the UK, the US, Europe, and Asia Machine learning (ML) has rapidly transcended the research labs of academia to become a foundational pillar of modern technology. From recommendation engines and autonomous vehicles to fraud detection and personalised healthcare, machine learning techniques are increasingly ubiquitous, transforming how organisations operate. This surge in applications has fuelled an extraordinary global demand for ML professionals—data scientists, ML engineers, research scientists, and more. In this article, we’ll examine how the UK machine learning job market compares to prominent international hubs, including the United States, Europe, and Asia. We’ll explore hiring trends, salary ranges, workplace cultures, and the nuances of remote and overseas roles. Whether you’re a fresh graduate aiming to break into the field, a software engineer with an ML specialisation, or a seasoned professional seeking your next challenge, understanding the global ML landscape is essential for making an informed career move. By the end of this overview, you’ll be equipped with insights into which regions offer the best blend of salaries, work-life balance, and cutting-edge projects—plus practical tips on how to succeed in a domain that’s constantly evolving. Let’s dive in.