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: We're seeking a skilled Machine Learning Engineer to join our AI Research team and build the data infrastructure that powers the training of cutting-edge video generation models. In this role, you'll develop offline jobs dedicated to training large generative models, manage training cluster code, and create data loaders to handle large-scale video datasets. Being an early member of our AI Research team will give you the opportunity to build foundational, state-of-the-art machine systems 0 to 1. Key Responsibilities:
- Design and develop robust data pipelines to support the efficient handling and processing of video data, ensuring high-quality data input for model training.
- Build and optimize systems for video frame extraction and other pre-processing steps to prepare data for training workflows.
- Create and manage data loaders for large-scale video datasets, focusing on speed and efficiency to support various model training requirements.
- Implement feature engineering techniques that enhance data quality and diversity, aiding in model accuracy and performance.
- Collaborate with research and engineering teams to scale data infrastructure and enable seamless experimentation and model iterations.
- Write and maintain cluster code to support high-performance training operations, including resource allocation and management.
Requirements:
- Bachelor's or Master's degree in Computer Science, Data Engineering, Machine Learning, or a related field.
- 3+ years of professional experience in software engineering, data engineering, or ML infrastructure development.
- Strong programming skills, particularly in Python, with proven experience with data pre-processing and feature engineering, ideally within video or image data contexts.
- Professional experience working with large-scale data processing frameworks, deep-learning systems, offline model training workflows, data loaders, and cluster infrastructure.
Benefits:
- Comprehensive medical, dental, and vision plans
- 401K with employer match
- Commuter Benefits
- Catered lunch multiple days per week
- Dinner stipend every night if you're working late and want a bite!
- Doordash DashPass subscription
- Health & Wellness Perks (Talkspace, Kindbody, One Medical subscription, HealthAdvocate, Teladoc)
- Multiple team offsites per year with team events every month
- Generous PTO policy and flexible WFH days
Captions provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. Please note benefits apply to full time employees only.