Senior Applied Research Engineer - Video

Synthesia is the world’s leading AI video platform for business, used by over 90% of the Fortune 100. Founded in 2017, the company is headquartered in London, with offices and teams across Europe and the US.
As AI continues to shape the way we live and work, Synthesia develops products to enhance visual communication and enterprise skill development, helping people work better and stay at the center of successful organizations.
Following our recent Series E funding round, where we raised $200 million, our valuation stands at $4 billion. Our total funding exceeds $530 million from premier investors including Accel, NVentures (Nvidia's VC arm), Kleiner Perkins, GV, and Evantic Capital, alongside the founders and operators of Stripe, Datadog, Miro, and Webflow.

About the role
As an Applied Research Engineer in our Video team, you will help build the next generation of production-grade foundation models for human-centric video generation.
You will join a highly focused team working at the intersection of large-scale generative modeling, distributed systems, and production engineering. Our mission is to develop and optimize video base models that power realistic, controllable, and emotionally expressive synthetic humans at scale.
This is not pure research. This is applied research with direct product impact.
You will work on advancing training recipes, scaling distributed systems, improving evaluation frameworks, and optimizing inference to ensure our models are high quality, stable, and efficient enough for real-world deployment. Your work will directly influence models used by tens of thousands of businesses worldwide. What you’ll do
You will own and execute end-to-end research and engineering projects, from hypothesis to production impact. This includes:
Developing and scaling latent video diffusion models tailored for human-centric video generation

Designing conditioning mechanisms to improve control (pose, emotion, script, camera) without sacrificing fidelity

Advancing distributed training strategies (DDP, FSDP, DeepSpeed, sequence parallelism) under real compute constraints

Improving training stability at multi-node scale

Designing rigorous evaluation frameworks combining automated metrics and structured human evaluation

Optimizing inference for low latency, high resolution, and cost efficiency

Running controlled ablations and experiments to drive high-signal modeling decisions

Contributing to high engineering standards: reproducibility, experiment tracking, CI/CD, monitoring

You will be expected to move fast, run multiple hypotheses in parallel, identify signal early, and focus on outcomes rather than exploration for its own sake. What we’re looking for
Must-have
Strong experience training deep learning models at scale

Strong Python and PyTorch skills

Hands-on experience with diffusion models (image domain required; video preferred)

Experience with large scale multi-GPU / multi-node training

Good understanding of distributed training (DDP, FSDP, DeepSpeed or similar)

Ability to design controlled experiments and interpret noisy results

Nice-to-have
Experience with video diffusion models

Experience in avatar or human-centric generation

Familiarity with world / interactive models

Experience with GANs or VAEs

Experience optimizing inference systems for production
Our stack
Python, PyTorch, CUDA

DeepSpeed, distributed training & inference

Sequence parallelism

AWS, SLURM, Docker

GitHub, CI/CD pipelines

Who you are
You are research-driven but outcome-focused

You care about shipping, not just publishing

You can explore multiple ideas quickly and drop low-signal directions early

You communicate clearly and present results scientifically

You operate independently but collaborate actively across teams

Why join us?
Build production-scale video foundation models in a fast-growing Generative AI company

Work on human-centric video generation with real-world impact