About this role.
Synthesia is hiring a Senior Applied Research Engineer to develop production-grade foundation models for human-centric video generation. The role combines applied machine learning research, distributed systems, and production engineering, with direct influence on video models used by enterprise customers. Key work includes scaling latent video diffusion training, improving controllability and evaluation, and optimizing inference for quality, latency, and cost. The engineer will independently run high-signal experiments while collaborating with technical teams in a fast-moving, high-ownership environment.
Role DNA
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Core skills
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Sample interview questions
I would begin by isolating whether the issue originated in data quality, optimizer settings, numerical precision, distributed synchronization, or the model architecture. I would use reproducible runs, targeted metric and gradient monitoring, smaller controlled experiments, and ablations before validating the fix at multi-node scale.
I would evaluate conditioning approaches such as cross-attention, adapter modules, control-specific encoders, and classifier-free guidance strategies for inputs including pose, emotion, script, and camera signals. I would compare them through controlled ablations using both fidelity metrics and structured human evaluation of adherence to each control.
The choice depends on model size, activation and optimizer-state memory, network bandwidth, cluster topology, checkpointing requirements, and implementation maturity. DDP is often efficient for models that fit per device, while FSDP or DeepSpeed becomes more appropriate when parameter, optimizer, or activation sharding is needed to scale training reliably.
I would combine automated measures for temporal consistency, identity preservation, lip synchronization, perceptual quality, and conditioning adherence with carefully designed human-rater studies. The framework should use representative prompts and failure cases, track regressions across model versions, and ensure that metrics correlate with user-perceived quality.
I prioritize hypotheses by expected impact, uncertainty, compute cost, and alignment with product needs. I run low-cost experiments to identify signal early, define clear stop criteria for weak directions, and reserve larger-scale training for approaches that demonstrate measurable improvements and deployment potential.
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
Tackle hard problems in scaling, stability, and controllability
Influence the direction of next-generation synthetic human technology
Join a highly technical, high-ownership environment where your work ships
If you want to work on cutting-edge generative video models and see your research power real-world products, we’d love to talk.
Our culture
At Synthesia we’re passionate about building, not talking, planning or politicising. We strive to hire the smartest, kindest and most unrelenting people and let them do their best work without distractions. Our work principles serve as our charter for how we make decisions, give feedback and structure our work to empower everyone to go as fast as possible. You can find out more about these principles here.
Serving 50,000+ customers (and 50% of the Fortune 500)
We’re trusted by leading brands such as Heineken, Zoom, Xerox, McDonald’s and more. Read stories from happy customers and what 1,200+ people say on G2.
Proprietary AI technology
Since 2017, we’ve been pioneering advancements in Generative AI. Our AI technology is built in-house, by a team of world-class AI researchers and engineers. Learn more about our AI Research Lab and the team behind.
AI Safety, Ethics and Security
AI safety, ethics, and security are fundamental to our mission. While the full scope of Artificial Intelligence’s impact on our society is still unfolding, our position is clear: People first. Always. Learn more about our commitments to AI Ethics, Safety & Security.
Annual salary information is not provided for this position. Explore salary ranges for similar roles in our Salary Directory ›
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