Senior / Staff / Principal Software Engineer

Flux Computing
London
1 year ago
Applications closed

Related Jobs

View all jobs

Principal Consultant, Advanced Analytics: Data Science and AI

Principal Data Scientist

Research Assistant/Associate in Data Science and Computational Neuroscience

Senior Data Scientist

Senior Research Scientist: Data Science and Machine Learning AIP

Senior Quantitative Data Scientist

Company Overview

Flux is pioneering a new class of AI accelerators called Optical Tensor Processing Units (OTPUs). We’ve already developed functioning prototypes and are now scaling our operations in London. Our work environment rewards innovation, speed, and bold thinking.


The role

We are seeking highly experienced and motivated Software Engineers to design and build the software architecture for our next-generation OTPUs. This role demands deep expertise in C and C++ programming, low-level programming, compiler construction, and optimisation techniques. The ideal candidate will have a strong background in computer science, electrical engineering, telecoms engineering, mathematics, or a related field, combined with significant experience in machine learning and a passion for high-pace environments. You’ll integrate deeply with the hardware team to ensure smooth interaction between compiler software and the optical hardware. You’ll also help shape our machine learning tools so they run efficiently on the OTPU.


Responsibilities

  • Design and implement the software and compiler frameworks that allow AI models to run optimally on our optical hardware.
  • Identify bottlenecks in performance and apply advanced compiler techniques, such as code generation, scheduling, and vectorisation.
  • Work closely with hardware engineers to align compiler requirements with the OTPU design; ensure software can fully exploit hardware capabilities.
  • Extend and maintain relevant machine learning libraries so users can easily leverage OTPU advantages.
  • Conduct code reviews, mentor team members, and drive best practices around compiler construction and performance tuning.
  • Keep abreast of industry trends in GPU, AI, and optical computing; contribute ideas for future directions and improvements.


Skills & Experience

  • 5+ years of experience in software engineering with a focus on C/C++ programming.
  • Extensive experience in compilers, low-level programming, and optimisation techniques.
  • Proven expertise in machine learning and its applications in high-performance computing.
  • Strong problem-solving skills and the ability to think critically and creatively.
  • Experience in high-pace, dynamic work environments.
  • Bachelor's degree in computer science, electrical engineering, telecoms engineering, mathematics, or a related field.
  • Excellent teamwork and communication skills, with the ability to collaborate effectively with cross-functional teams.
  • Personal projects are a key differentiating factor and hold more weight than other requirements.


Details

  • Competitive salary ranging from £145k+, depending on experience.
  • Stock options in a rapidly growing AI company.
  • Comprehensive healthcare insurance.
  • 25 days PTO policy plus bank holidays.
  • Based in our new 5,000 square foot office in the AI hub of Kings Cross, London.
  • Bonus additional salary of £12,000 per year if you’re based within a 20-minute commute of the office.
  • Private use of our 3D printer.


If you’re passionate about compilers, high-performance computing, and redefining what’s possible in AI, we’d love to talk. Apply now to join Flux and help shape the future of optical computing.

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.