Machine Learning Engineering, Training Data Infrastructure
CaptionsCaptions 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.