Member of Technical Staff, MLOps

Captions is the leading video AI company, building the future of video creation. Over 10 million creators and businesses have used Captions to create videos for social media, marketing, sales, and more. We're on a mission to serve the next billion.

We are a rapidly growing team of ambitious, experienced, and devoted engineers, researchers, designers, marketers, and operators based in NYC. You'll join an early team and have an outsized impact on the product and the company's culture.

We’re very fortunate to have some the best investors and entrepreneurs backing us, including Index Ventures (Series C lead), Kleiner Perkins (Series B lead), Sequoia Capital (Series A and Seed co-lead), Andreessen Horowitz (Series A and Seed co-lead), Uncommon Projects, Kevin Systrom, Mike Krieger, Lenny Rachitsky, Antoine Martin, Julie Zhuo, Ben Rubin, Jaren Glover, SVAngel, 20VC, Ludlow Ventures, Chapter One, and more.

Check out our latest financing milestone and some other coverage:

The Information: 50 Most Promising Startups

Fast Company: Next Big Things in Tech

The New York Times: When A.I. Bridged a Language Gap, They Fell in Love

Business Insider: 34 most promising AI startups

Time: The Best Inventions of 2024

** Please note that all of our roles will require you to be in-person at our NYC HQ (located in Union Square) **

About the Role:

Captions seeks an exceptional MLOps Research Engineer (MOTS) to architect and scale the machine learning infrastructure for our rapidly growing creative platform used by millions. You'll own the development of our distributed training systems, optimize our rapidly growing GPU clusters, and build performant inference pipelines that power our cutting-edge multimodal video diffusion models. As a key member of our ML Research team in a fast-growing Series C startup, you'll create foundational infrastructure enabling rapid research iteration while maintaining production-grade reliability and efficiency. We're already training large-scale models and are excited to dramatically expand our infrastructure capabilities.

Key Responsibilities:

Core Systems Development:

• Develop and optimize distributed training frameworks integrating multiple modalities (video, audio, text, and structured metadata)

• Build flexible systems for cross-modal training orchestration and efficient experimentation

• Design reproducible training environments with versioned dependencies and configurations

• Implement comprehensive testing frameworks for validating model training correctness and performance

• Create infrastructure for systematic model quality assessment and performance benchmarking

Infrastructure Development:

• Design and implement flexible training orchestration systems that balance research agility with large-scale model training

• Build robust monitoring and observability systems for complex training and inference pipelines

• Design and manage GPU clusters optimized for distributed training of multimodal models

• Build out comprehensive automated metrics collection and alerting across our ML stack

System Optimization:

• Profile and optimize model training throughput using mixed precision, gradient checkpointing, and advanced memory techniques

• Develop custom CUDA and Triton kernels to accelerate critical compute paths

• Implement creative solutions for cost optimization across spot instances and reserved capacity

• Design and optimize real-time inference systems enabling fast research iteration cycles

Research & Product Impact:

• Build infrastructure enabling rapid testing of research hypotheses

• Create systems supporting close collaboration between infrastructure and research teams

• Develop frameworks for reprodu