Member of Technical Staff, Training Data Infrastructure

Company Name: Captions
Location: Union Square, New York City, United States (In-person at NYC HQ)
Job Type: Full-time
Salary Range: $170K – $250K yearly (Offers Equity)
Industry: AI Research / Video AI / Software (specifically MLOps/Infrastructure for video and multimodal generative models)

Job Overview

Captions is a pioneering company at the forefront of AI Research, Video AI, and Software, dedicated to transforming how video content is created and understood using groundbreaking generative models. We are seeking an exceptional Member of Technical Staff, Training Data Infrastructure (Research Engineer (MOTS)) to join our team in the heart of Union Square, New York City. This Full-time, Mid-Level to Senior-Level role offers a pivotal opportunity to design, build, and optimize the scalable ML infrastructure essential for processing video and multimodal training data at massive scale, directly impacting the capabilities of our groundbreaking video AI software.

As a Member of Technical Staff, Training Data Infrastructure, you will be instrumental in creating performant pipelines for processing video and multimodal data, designing distributed systems for rapidly growing datasets, and building efficient data loading systems optimized for GPU training throughput. You will leverage your strong programming skills, expertise in distributed computing, and passion for improving system performance to enable rapid research iteration and seamless deployment of AI innovations to production. If you love tackling hard technical problems head-on, thrive in a fast-paced, research-driven environment, and are eager to work directly with researchers and engineers passionate about building great systems, Captions invites you to contribute your expertise to our groundbreaking mission.

Duties and Responsibilities

• Build performant pipelines for processing video and multimodal training data at scale.
• Design distributed systems that scale seamlessly with rapidly growing datasets.
• Create efficient data loading systems optimized for GPU training throughput.
• Implement comprehensive telemetry for video processing and training pipelines to ensure robust monitoring.
• Create foundation data processing systems that intelligently cache computations for efficiency.
• Build robust data validation and quality measurement systems to ensure data integrity.
• Design systems for data versioning and reproducing complex training runs.
• Develop efficient storage and compute patterns for high-dimensional data.
• 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 training data characteristics.
• 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.
• Apply a Bachelor’s or Master’s degree in Computer Science, Machine Learning, or related field.
• Possess 3+ years experience in ML infrastructure development or large-scale data engineering.
• Demonstrate strong programming skills, particularly in Python and distributed computing frameworks.
• Apply expertise in building and optimizing high-throughput data pipelines.
• Showcase proven experience with video/image data pre-processing and feature engineering.
• Possess deep knowledge of machine learning workflows, including model training and data loading systems.
• Maintain a track record in performance optimization and system scaling.
• Possess experience with cluster management and distributed computing.
• Possess a background in MLOps and infrastructure monitoring.
• Demonstrate a proven ability to build reliable, large-scale data processing systems.
• 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.

Qualifications

• Experience Level: Mid-Level to Senior-Level (3+ years experience in relevant ML infrastructure or large-scale data engineering).
• Education Requirement: Bachelor’s or Master’s degree in Computer Science, Machine Learning, or related field.
• Required Skills:
  • 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: Loves tackling hard technical problems head-on; Takes ownership while knowing when to loop in teammates; Gets excited about improving system performance; Wants to work directly with researchers and engineers who are equally passionate about building great systems.
  • Key Responsibilities involve: Building performant pipelines for processing video and multimodal training data at scale; Designing distributed systems that scale seamlessly with rapidly growing datasets; Creating efficient data loading systems optimized for GPU training throughput; Implementing comprehensive telemetry for video processing and training pipelines; Creating foundation data processing systems that intelligently cache computations; Building robust data validation and quality measurement systems; Designing systems for data versioning and reproducing complex training runs; Developing efficient storage and compute patterns for high-dimensional data; Owning and improving end-to-end training pipeline performance; Building systems for efficient storage and retrieval of video training data; Building frameworks for systematic data and model quality improvement; Developing infrastructure supporting fast research iteration cycles; Building tools and systems for deep understanding of training data characteristics; Building infrastructure enabling rapid testing of research hypotheses; Creating systems for incorporating user feedback into training workflows; Designing measurement frameworks that connect model improvements to user outcomes; Enabling systematic experimentation with direct user feedback loops.

Salary and Benefits

Captions offers an exceptional annual salary ranging from $170K – $250K yearly for this Full-time Member of Technical Staff, Training Data Infrastructure position. The compensation package also offers Equity in the company. We believe in rewarding top talent and fostering a dynamic work environment. Beyond salary and equity, Captions is committed to providing a comprehensive benefits package designed to support your overall well-being and professional growth, which typically includes robust health, dental, and vision insurance, generous paid time off, and opportunities for continuous professional development at the cutting-edge of AI research.

Working Conditions

This is a Full-time position based in-person at NYC HQ in Union Square, New York City, United States. You will work within a highly collaborative and innovative office environment, engaging directly with researchers, ML engineers, and software development teams. The role demands exceptional technical expertise in large-scale data engineering and ML infrastructure, strong programming skills, and the ability to design and optimize critical data pipelines. You will be expected to tackle hard technical problems, show ownership, and contribute to the deployment of cutting-edge AI models. Standard business hours are generally observed.

Why Work with Us

At Captions, you’re not just joining a company; you’re becoming part of a team that’s redefining the future of video content creation through AI Research and cutting-edge Video AI technology. We are a pioneering force, building sophisticated software that empowers users with unimaginable creative capabilities, with a specific focus on MLOps/Infrastructure for video and multimodal generative models.

We offer a challenging yet incredibly rewarding environment where your expertise in Training Data Infrastructure, distributed systems, and data pipeline optimization will be highly valued. You will be empowered to design scalable systems for rapidly growing datasets, build efficient data loading systems for GPU training, and directly contribute to state-of-the-art generative AI. If you are a results-driven engineer with a clear passion for pushing the boundaries of AI infrastructure, and eager to make a tangible impact on a rapidly evolving software landscape, Captions offers an unparalleled opportunity for your next career chapter.