Mirage
Research Engineer, Generative Video
About this role
Mirage is hiring a Research Engineer to build and optimize large-scale generative video models for production. You'll work on novel modeling approaches, training infrastructure, and inference optimization to enable real-time, ultra-low latency video generation at scale.
What you'll do
- Train and optimize large-scale video and multimodal diffusion/autoregressive models
- Improve efficiency across training and inference through distillation, quantization, and pruning techniques
- Build and maintain distributed training systems with optimized GPU utilization and parallelism
- Develop experimentation, evaluation, and debugging tooling for model development
- Translate research prototypes into robust, production-ready systems
- Monitor and improve model performance in real-world production usage
What they're looking for
- Deep learning systems and infrastructure design
- PyTorch and CUDA programming
- Triton kernel optimization
- Distributed training frameworks (FSDP, etc.)
- Large model scaling and inference optimization
- Performance profiling and debugging
- Video generation or diffusion model experience
- Rapid prototyping and productionization
Benefits
- Comprehensive medical, dental, and vision coverage
- 401K with employer match
- Catered lunch multiple days per week and dinner stipends
- Commuter benefits and Grubhub subscription
- Health and wellness perks
- Multiple team offsites and monthly team events
- Generous PTO policy
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Mirage
Mirage builds an AI-native video platform that leverages generative media and large language models to enable sophisticated video production, editing, and creative workflows. The company is hiring backend engineers, full-stack software engineers, ML engineers, and iOS developers to advance their AI-driven platform and enhance user experiences in web-based and mobile media creation.
View all jobs at MirageLikely interview questions
- Walk us through a large-scale model optimization project you led—what was the bottleneck and how did you measure success?
- How have you approached inference latency reduction for generative models, and what trade-offs did you consider?