Senior Applied Research Engineer - Video

• 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-driven mindset combined with a strong focus on practical outcomes and shipping production solutions.
• Ability to explore multiple approaches quickly, identify promising directions, and discontinue low-value experiments when appropriate.
• Strong scientific communication skills, with the ability to clearly present experimental results and technical conclusions.
• High degree of autonomy, ownership, adaptability, and initiative, combined with a collaborative approach to working across teams.

Benefits

• Fully remote working environment within Europe.
• Full-time employment.
• Opportunity to build and work on production-scale video foundation models at the forefront of Generative AI.
• Direct opportunity to influence next-generation human-centric video generation technology.
• Work on challenging technical problems involving scalability, model stability, controllability, evaluation, and inference optimization.
• High-ownership environment where research and engineering contributions are designed to reach real-world products.
• Opportunity to collaborate with highly technical AI researchers and engineers.
• Exposure to large-scale machine learning infrastructure, distributed computing, and production AI systems.
• Opportunity to work on technology serving tens of thousands of businesses worldwide.
• Fast-paced environment that encourages autonomy, experimentation, scientific thinking, and measurable impact.
• Opportunity to contribute to AI technology with a strong focus on safety, ethics, security, and people-first development.