Senior Applied Research Engineer - Video Team

Synthesia; Zürich; Vor Ort; CHF 73.226 - 109.839; Vollzeit

Benefits dieser Stelle

Competitive compensation (salary + stock options + bonus)

25 days of annual leave + public holidays

Great company culture with options for team events

Zusammenfassung

Synthesia is seeking a Research Engineer in Zürich to join our Video Pre-Training team, where you will help build the next generation of production-grade foundation models for human-centric video generation. You will manage end-to-end research and engineering projects, enhance training methodologies, and optimize evaluation frameworks for high-performing models. Candidates should possess strong deep learning skills, particularly with Python and PyTorch, along with experience in diffusion models. The position offers competitive compensation and the flexibility of remote work within Europe.

Qualifikationen

• 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.

Aufgaben

• Own and execute end-to-end research and engineering projects.
• Develop and scale latent video diffusion models for human-centric video generation.
• Design conditioning mechanisms to improve control without sacrificing fidelity.
• Advance distributed training strategies under real compute constraints.
• Improve training stability at multi-node scale.
• Design rigorous evaluation frameworks combining automated metrics and human evaluation.
• Optimize inference for low latency and cost efficiency.

Kenntnisse

Training deep learning models

Python

PyTorch

Diffusion models

Multi-GPU training

Distributed training

Tools

CUDA

DeepSpeed

AWS

Docker

GitHub

Jobbeschreibung

About The Role

As a Research Engineer in our Video Pre-Training 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 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

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

Benefits

• Competitive compensation (salary + stock options + bonus)
• Fully remote from Europe or hybrid work setting with an office in London, Amsterdam, Zurich, Munich
• 25 days of annual leave + public holidays
• Great company culture with the option to join regular planning and socials at our hubs
• + other benefits depending on your location