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Mirage is an AI-native video platform that uses natural language to intelligently orchestrate production and editing, leveraging generative models to execute professional-grade creative decisions. The company has raised $75M from top-tier investors including Sequoia, Andreessen Horowitz, and Kleiner Perkins, and was recognized on Forbes' AI 50 list.
As a Research Engineer on the Generative Video team, you'll work at the intersection of research and systems engineering to build and scale the infrastructure powering Mirage's video generation models. This role focuses on making advanced models faster, more efficient, and capable of ultra-low latency, real-time generation—translating cutting-edge research into production systems that serve millions of users.
Key responsibilities include training and optimizing large-scale video and multimodal models; improving efficiency across training and inference pipelines (memory, latency, cost); implementing acceleration techniques such as distillation, quantization, and pruning for diffusion and autoregressive generation; building and maintaining distributed training systems; optimizing GPU utilization, parallelism, and throughput; developing tooling for experimentation, evaluation, and debugging; and monitoring model performance in real-world usage.
You'll need a BS/MS/PhD in CS, ML, or related field with 2+ years of professional industry experience. Strong expertise required in deep learning systems and infrastructure, PyTorch, CUDA, Triton, and distributed training frameworks (FSDP, etc.). Experience scaling and optimizing large models under low-latency inference constraints is essential, along with strong debugging and performance profiling skills and the ability to move quickly from prototype to production.
This is a full-time, in-person role based at Mirage's NYC headquarters in Union Square. The company offers comprehensive medical, dental, and vision coverage, 401K matching, commuter benefits, catered meals, and a generous PTO policy.
