Machine Learning Engineer (5+ years of experience)

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)

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 learning framework (PyTorch, TensorFlow).

• Model Optimization

  • Familiarity with compression techniques (quantization, pruning, distillation) for large-scale models.

  • Experienc