Senior Machine Learning Engineer - AI & GPU Performance

Synthesia
Harrow
4 months ago
Applications closed

Related Jobs

View all jobs

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Synthesia is on a mission to make video easy for everyone. Born in an AI lab, our AI video communications platform simplifies the entire video production process, making it easy for everyone, regardless of skill level, to create, collaborate, and share high-quality videos.

We are looking for a ML Performance Engineer to join our team of 40+ Researchers and Engineers within the R&D Department. As a ML Performance Engineer, you will contribute to the design and development of high-performance solutions and own one or more projects for computationally optimizing large-scale model training and inference pipelines.

Your responsibilities will include:

  • Evaluating, profiling, and optimizing compute resource usage for cost and time efficiency at training and inference times.
  • Developing customized efficient solutions for inference pipelines and introducing or enhancing tooling for achieving optimal computational performance.
  • Driving the adoption of best practices for large-model training, including checkpointing, gradient accumulation, and memory optimization.
  • Introducing or enhancing tooling for distributed training, performance monitoring, and logging.
  • Designing and implementing techniques for model parallelism, data parallelism, and mixed-precision training.
  • Keeping updated on the latest research in model compression and advanced optimization methods.

To be successful in this role, you should be a ML engineer passionate about high-performance computing, with a background in Computer Science/Engineering and 3+ years of industry experience. You should have experience optimizing large models, developing CUDA/Triton kernels, and working with DL compilers.

We offer attractive compensation, private health insurance, hybrid work setting, and opportunities for career growth. If you are interested in working for a company that is impacting businesses at a rapid pace across the globe, please apply.


#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.