Senior Applied Research Engineer - Video
Role Summary
Join Synthesia as a Senior Applied Research Engineer and develop advanced video generation models in Germany, with opportunities for impactful research.
About the Organisation
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.
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.
- 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
Key Responsibilities
You will own and execute end-to-end research and engineering projects, from hypothesis to production impact. This includes:
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.
- 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
Minimum Requirements
Experience optimizing inference systems for production
- 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
- Experience with video diffusion models
- Experience in avatar or human-centric generation
- Familiarity with world / interactive models
- Experience with GANs or VAEs
- 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
Eligibility Criteria
Eligibility details are unclear; the job may be open to international applicants without specifying requirements.
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