Team Lead - ML Optimisaton team London

From your everyday PowerPoint presentations to Hollywood movies, AI will transform the way we create and consume content.

Today, people want to watch and listen, not read — both at home and at work. If you’re reading this and nodding, check out our brand video .

Despite the clear preference for video, communication and knowledge sharing in the business environment are still dominated by text, largely because high-quality video production remains complex and challenging to scale—until now….

Meet Synthesia

We’re 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. Whether it’s for delivering essential training to employees and customers or marketing products and services, Synthesia enables large organizations to communicate and share knowledge through video quickly and efficiently. We’re trusted by leading brands such as Heineken, Zoom, Xerox, McDonald’s and more.

About the role

As a Team Lead you will join a team of 40+ Researchers and Engineers within the R&D Department working on cutting edge challenges in the Generative AI space, with a focus on creating highly realistic, emotional and life-like Synthetic humans through text-to-video. Within the team you’ll have the opportunity to work on the applied side of our research efforts and directly impact our solutions that are used worldwide by over 55,000 businesses.

If you are an expert in training Diffusion models for generative AI, this is your chance. This is an opportunity to work for a company that is impacting businesses at a rapid pace across the globe.

What will you be doing?

As our Team Lead for the ML Optimisation team, you will define the technical vision and roadmap for large-scale model training, inference and optimisation. By partnering with researchers and research teams you’ll identify high-impact initiatives and push the boundaries of model performance. You’ll also work on re-implementing models in an efficient manner by using PyTorch and underlying technologies like Cuda Kernels, Torch compilation techniques.

This would include:

• Evaluating and optimising compute resource usage (e.g., Hopper GPUs) for cost and time efficiency at training and inference times.
• Driving the adoption of best practices for large-model training, including checkpointing, gradient accumulation, and memory optimisation among others.
• Introducing or enhancing tooling for distributed training, performance monitoring, and logging (e.g., DeepSpeed, PyTorch Distributed).
• Designing and implementing techniques for model parallelism, data parallelism, and mixed-precision training.
• Keeping the team updated on the latest research in model compression (e.g., quantization, pruning) and advanced optimisation methods.
• Leading experiments to validate novel approaches and ensure models remain at the forefront of performance and reliability.
• Managing, mentoring, and growing a team of ML optimisation engineers and specialists.
• Providing technical guidance, setting goals, reviewing code, and leading by example in writing clean, efficient, maintainable code.

Who are you?

• You are a natural leader, with experience leading teams or high impact projects (beyond POC).
• You have a background in Computer Vision / Computer Science and 3+ years of industry experience. (PhD preferred).
• You have worked on optimising large models for over 3 years.
• You have experience with optimising models that were trained on distributed systems.
• You have strong experience in the video space (Diffusion models / GAN’s), preferably in the Avatar or generative video or image domain.
• Experience with Triton, TensorRT, TensorLLM.
• Familiar with distributed training tools like DDP, Deepspeed, Accelerate or similar.
• You are interested in doing research, trying new things and pushing the boundaries, going beyond what’s already known.
• You have experience in using most modern frameworks for machine learning and deep learning.
• You have great coding skills in Python, preferably C++ and Cuda.
• And you care about writing clean, efficient code.

The good stuff…

• Attractive compensation (salary + stock options + bonus).
• Private Health Insurance in London.
• Hybrid work setting with an office in London.
• 25 days of annual leave + public holidays.
• Work in a great company culture with the option to join regular planning and socials at our hubs.
• A generous referral scheme when you know people that are amazing for us.
• Strong opportunities for your career growth.

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