Member of Technical Staff, ML Engineer
MirageAbout the role
Mirage is the leading AI short-form video company. We’re building full-stack foundation models and products that redefine video creation, production and editing. Over 20 million creators and businesses use Mirage’s products to reach their full creative and commercial potential.
We are a rapidly growing team of ambitious, experienced, and devoted engineers, researchers, designers, marketers, and operators based in NYC. As an early member of our team, you’ll have an opportunity to have an outsized impact on our products and our company's culture.
Our Products
Our Technology
Press Coverage
Our Investors
We’re very fortunate to have some the best investors and entrepreneurs backing us, including Index Ventures, Kleiner Perkins, Sequoia Capital, Andreessen Horowitz, Uncommon Projects, Kevin Systrom, Mike Krieger, Lenny Rachitsky, Antoine Martin, Julie Zhuo, Ben Rubin, Jaren Glover, SVAngel, 20VC, Ludlow Ventures, Chapter One, and more.
** Please note that all of our roles will require you to be in-person at our NYC HQ (located in Union Square)
We do not work with third-party recruiting agencies, please do not contact us**
About the role:
Captions is seeking a Machine Learning Engineer to partner closely with our Researchers and bring large-scale multimodal video diffusion models into production. You’ll be responsible for optimizing and deploying state-of-the-art generative models (tens to hundreds of billions of parameters) to deliver low-latency, high-throughput inference at scale. This is a unique opportunity to work on cutting-edge AI—spanning audio-video generation, diffusion architectures, and temporal modeling—and ensure these innovations reach millions of creators worldwide.
Responsibilities:
Inference & Deployment
Develop high-performance GPU-based inference pipelines for large multimodal diffusion models.
Build, optimize, and maintain serving infrastructure to deliver low-latency predictions at large scale.
Collaborate with DevOps teams to containerize models, manage autoscaling, and ensure uptime SLAs.
Model Optimization & Fine-Tuning
Leverage techniques like quantization, pruning, and distillation to reduce latency and memory footprint without compromising quality.
Implement continuous fine-tuning workflows to adapt models based on real-world data and feedback.
Production MLOps
Design and maintain automated CI/CD pipelines for model deployment, versioning, and rollback.
Implement robust monitoring (latency, throughput, concept drift) and alerting for critical production systems.
Performance & Scaling
Explore cutting-edge GPU acceleration frameworks (e.g., TensorRT, Triton, TorchServe) to continuously improve throughput and reduce costs.
Requirements:
Technical Expertise
Proven experience deploying deep learning models on GPU-based infrastructure (NVIDIA GPUs, CUDA, TensorRT, etc.).
Strong knowledge of containerization (Docker, Kubernetes) and microservice architectures for ML model serving.
Proficiency with Python and at least one deep le
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