Senior Applied Research Engineer - Video

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Applied Research Engineer - Video based in Switzerland. As a Senior Applied Research Engineer, you will help build the next generation of production-grade foundation models for human-centric video generation. You will work at the intersection of generative AI research, large-scale distributed systems, and production engineering. Your work will focus on developing realistic, controllable, and expressive video generation models that can operate reliably at scale. You will own research and engineering projects end to end, translating hypotheses and experiments into measurable product impact. The role combines advanced modeling, distributed training, evaluation, inference optimization, and rigorous experimentation. You will operate in a highly technical, high-ownership environment where research is expected to move quickly toward real-world deployment. Your contributions will directly influence AI-powered video products used by businesses around the world. Develop and scale latent video diffusion models designed for human-centric video generation. Design advanced conditioning mechanisms that improve control over elements such as pose, emotion, scripts, and camera movement while maintaining high visual fidelity. Lead end-to-end applied research and engineering projects, from developing hypotheses and running experiments through to production implementation and measurable impact. Develop and optimize distributed training strategies using technologies such as DDP, FSDP, DeepSpeed, and sequence parallelism. Improve training stability and efficiency across large-scale, multi-GPU and multi-node environments while working within real-world compute constraints. Design robust evaluation frameworks combining automated metrics with structured human evaluation to assess model quality and performance. Optimize model inference for low latency, high resolution, scalability, and cost efficiency in production environments. Run controlled experiments, ablations, and parallel research hypotheses to identify high-value signals and guide modeling decisions. Establish and maintain strong engineering practices around reproducibility, experiment tracking, CI/CD, monitoring, and production reliability. Translate research findings into practical improvements for production-grade generative video systems. Collaborate actively with researchers, engineers, and cross-functional teams while maintaining a high degree of individual ownership. Move quickly between promising research directions, identifying low-signal approaches early and prioritizing work based on measurable outcomes. Requirements Strong professional experience training deep learning models at scale, ideally in a research or production environment. Strong programming skills in Python and hands-on expertise with PyTorch. Practical experience working with diffusion models, with image-generation experience required and video-generation experience strongly preferred. Proven experience with large-scale multi-GPU and multi-node model training. Strong understanding of distributed training frameworks and techniques such as DDP, FSDP, DeepSpeed, or comparable technologies. Ability to design controlled experiments, analyze noisy or ambiguous results, and make scientifically grounded modeling decisions. Experience with video diffusion models is an advantage. Experience with avatar generation, synthetic humans, or other human-centric generative AI applications is a plus. Familiarity with world models, interactive models, GANs, or VAEs is desirable. Experience optimizing inference systems for production deployment is an advantage. Strong understanding of CUDA and experience working within modern machine learning infrastructure. Ability to work effectively with technologies such as AWS, SLURM, Docker, CI/CD pipelines, and distributed training and inference systems. Research-dr