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) **

Overview

Captions seeks an exceptional Machine Learning Engineer to drive innovation in training data infrastructure. You'll conduct research on and develop sophisticated distributed training workflows and optimized data processing systems for massive video and multimodal datasets. Beyond pure performance, you'll develop deep insight into our data to maximize training effectiveness. As an early member of our ML Research team, you'll build foundational systems that directly impact our ability to train models powering video and multimodal creation for millions of users.

Key Responsibilities

Infrastructure Development:

• Build performant pipelines for processing video and multimodal training data at scale

• Design distributed systems that scale seamlessly with our rapidly growing video and multimodal datasets

• Create efficient data loading systems optimized for GPU training throughput

• Implement comprehensive telemetry for video processing and training pipelines

Core Systems Development:

• Create foundation data processing systems that intelligently cache and reuse expensive computations across the training pipeline

• Build robust data validation and quality measurement systems for video and multimodal content

• Design systems for data versioning and reproducing complex multimodal training runs

• Develop efficient storage and compute patterns for high-dimensional data and learned representations

System Optimization:

• Own and improve end-to-end training pipeline performance

• Build systems for efficient storage and retrieval of video training data

• Build frameworks for systematic data and model quality improvement

• Develop infrastructure supporting fast research iteration cycles

• Build tools and systems for deep understanding of our training data characteristics

Research & Product Impact:

• Build infrastructure enabling rapid testing of research hypotheses

• Create systems for incorporating user feedback into training workflows

• Design measurement frameworks that connect model improvements to user outcomes

• Enable systematic experimentation with direct user feedback loops

Preferred Qualifications:

Technical Background:

• Bachelor's or Master's degree in Computer Science, Machine Learning, or related field

• 3+ years experience in ML infrastructure development or large-scale data engineering

• Strong programming skills, particularly in Python and distributed computing frameworks

• Expertise in building and optimizing high-throughput data pipelines

• Proven experience with video/image data pre-processing and feature engineering

• Deep knowledge of machine learning workflows, including model training and data loading systems

System Development:

• Track record in performance optimization and system scaling

• Experience with cluster management and distributed computing

• Background in MLOps and infrastructure monitoring

• Demonstrated ability to build reliable, large-scale data processing systems

Engineering Approach:

• Love tackling hard technical problems head-on

• Take ownership while knowing when to loop in teammates

• Get excited about improving system performance

• Want to work directly with researchers and engineers who are equally passionate about building great systems

Team Culture

You'll work directly alongside our research and engineering teams in our NYC office. We've intentionally built a culture where infrastructure and data work is highly valued - your success will be measured by the reliability and performance of our systems, not by your ability to navigate politics. We're a team that loves diving deep into technical problems and emerging with practical solutions.

Our team values:

• Quick iteration and practical solutions

• Open discussion of technical approaches

• Direct access to decision makers

• Regular sharing of learnings, results, and iterative work

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.