Mirage
Research Engineer, Generative Video
About this role
Mirage is seeking an ML Engineer to build and optimize large-scale video generation models, focusing on making advanced generative systems faster and more efficient for production use. You'll work on novel modeling approaches, training infrastructure, and inference optimization to enable real-time video generation at scale.
What you'll do
- Train and optimize large-scale video and multimodal generative models
- Improve training and inference efficiency including memory, latency, and cost optimization
- Implement acceleration techniques such as distillation, quantization, and pruning for diffusion and autoregressive models
- Build and maintain distributed training systems with optimized GPU utilization and parallelism
- Develop tooling for experimentation, evaluation, and model debugging
- Translate research models into robust, production-ready systems and monitor real-world performance
What they're looking for
- Deep learning systems and infrastructure
- PyTorch and CUDA programming
- Triton kernel optimization
- Distributed training frameworks (FSDP, etc.)
- Model compression techniques (distillation, quantization, pruning)
- GPU profiling and performance optimization
- Low-latency inference systems
- Debugging and performance analysis
Benefits
- Medical, dental, and vision insurance
- 401(k) with employer match
- Commuter benefits
- Catered lunch multiple days per week
- Dinner stipend for late-night work
- Grubhub subscription and health & wellness perks
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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 your experience optimizing large models for low-latency inference—what techniques did you use and what were the results?
- Describe a time you debugged a performance issue in a distributed training system. How did you identify and resolve it?