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Machine Learning Engineer

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’re seeking a Machine Learning Engineer with demonstrated experience deploying and scaling large generative models. As part of our engineering team, you’ll work on inference optimization, high-throughput pipelines, and model-serving architectures that bring SOTA video-generation technology to millions of users around the world. If you’re passionate about applying cutting-edge, multimodal foundational models at scale—this role is for you.

Responsibilities

  • Deploy and optimize large generative models (e.g., diffusion, transformer-based) for real-time or batch inference.

  • Build and maintain end-to-end ML pipelines, from data ingestion to model serving in production environments.

  • Improve model performance through techniques like quantization, pruning, or distributed model inference.

  • Collaborate closely with cross-functional teams (infrastructure, product, research) to integrate advanced ML features into our video creation platform.

  • Implement MLOps best practices, including monitoring, logging, and robust CI/CD workflows for ML.

Preferred Qualifications

  • 5+ years of industry experience deploying and scaling large generative or deep learning models in production.

  • Strong software engineering skills in Python (or similar), with experience in cloud environments (AWS, GCP, or Azure).

  • Expertise in deep learning frameworks (e.g., PyTorch, TensorFlow), with a focus on inference optimization and high-throughput serving.

  • Familiarity with distributed training methods and large-scale data processing pipelines.

  • A passion for practical, results-driven ML solutions—delivering tangible value to end users rather than purely research outcomes.

Join us if you’re excited to shape the future of generative video and unlock the potential of large-scale ML systems for creators worldwide!

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.

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What You Should Know About Machine Learning Engineer, Captions

Are you ready to take your skills as a Machine Learning Engineer to the next level with Captions? Based in the heart of New York City, we are the leading video AI company revolutionizing how creators and businesses generate videos across various platforms. With over 10 million satisfied users, including marketers and social media influencers, we're on an ambitious mission to serve the next billion creators! At Captions, you’ll join a passionate team of engineers, researchers, and designers who are eager to make a real difference. In this role, you’ll be at the forefront of deploying and scaling groundbreaking generative models to optimize our video generation technology. Imagine the satisfaction of seeing your work empower millions of users! You will build and maintain robust end-to-end ML pipelines, improve model performance through innovative techniques, and collaborate across teams to integrate cutting-edge ML features into our platform. We’re looking for someone with at least 5 years of experience in this area, a strong grasp of deep learning frameworks, and, importantly, a zest for delivering practical solutions that users love. Our culture is dynamic, and with support from top investors and thought leaders in the tech industry, we’re creating an environment where you can thrive. If you're excited about being a part of a company that's shaping the future of video through advanced machine learning methods, we'd love to hear from you! Join us at Captions, where your ideas can truly shine.

Frequently Asked Questions (FAQs) for Machine Learning Engineer Role at Captions
What are the responsibilities of a Machine Learning Engineer at Captions?

As a Machine Learning Engineer at Captions, you will be responsible for deploying and optimizing large generative models for real-time or batch inference. You'll build and maintain end-to-end ML pipelines, improve model performance, and collaborate with cross-functional teams to integrate advanced ML features into our video creation platform.

Join Rise to see the full answer
What qualifications do I need to become a Machine Learning Engineer at Captions?

To succeed as a Machine Learning Engineer at Captions, you should have 5+ years of industry experience, strong software engineering skills in Python or similar languages, and expertise in deep learning frameworks like PyTorch or TensorFlow. Experience in cloud environments and familiarity with distributed training methods is also highly preferred.

Join Rise to see the full answer
What type of technologies will I work with as a Machine Learning Engineer at Captions?

At Captions, as a Machine Learning Engineer, you will engage with cutting-edge technologies including generative models, inference optimization techniques, and cloud platforms such as AWS, GCP, or Azure, all aimed at enhancing the capabilities of video AI solutions.

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What are the collaborative aspects of the Machine Learning Engineer role at Captions?

The Machine Learning Engineer at Captions will collaborate closely with multiple teams such as infrastructure, product, and research. This collaboration is crucial to ensure the integration of advanced ML features and to address the diverse needs of our video creation platform while driving innovation.

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What benefits do I get as a Machine Learning Engineer at Captions?

Captions offers an array of benefits for full-time Machine Learning Engineers, including comprehensive medical, dental, and vision plans, 401K with employer match, commuter benefits, catered lunches, health and wellness perks, and generous PTO policies among other exciting perks!

Join Rise to see the full answer
Common Interview Questions for Machine Learning Engineer
Can you explain your experience deploying generative models?

When discussing your experience, focus on specific projects where you successfully deployed generative models. Highlight the challenges you faced, the frameworks you used, and the impact your work had on real-world applications. Be sure to quantify your results, such as improvements in performance or user engagement.

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How have you optimized ML pipelines in previous roles?

Provide concrete examples of how you have optimized ML pipelines, using techniques such as quantization and pruning. Discuss the steps you took, the tools used, and the results achieved, emphasizing any improvements in inference speed or accuracy.

Join Rise to see the full answer
What deep learning frameworks are you proficient in?

Be prepared to discuss the deep learning frameworks you're most comfortable with, such as TensorFlow or PyTorch. Share specific projects you’ve worked on and why you preferred one framework over the other for different aspects of your projects.

Join Rise to see the full answer
How do you approach collaboration with cross-functional teams?

Discuss your communication style and how you foster collaboration with teams from diverse backgrounds, such as product and infrastructure. Share specific examples of past collaborations and highlight the importance of understanding different perspectives in achieving project goals.

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Can you describe an instance where you faced a significant challenge in model performance?

Share a specific challenge you encountered regarding model performance, such as issues with overfitting or latency. Discuss the steps you took to address the problem, the techniques you employed, and the eventual outcome.

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What MLOps practices do you implement in your work?

Talk about your familiarity with MLOps best practices such as CI/CD workflows, monitoring and logging, and how you’ve applied these practices in past projects. Highlight the importance of these practices in ensuring model reliability and deployment efficiency.

Join Rise to see the full answer
How do you stay informed about advancements in machine learning?

Discuss the resources you utilize to stay informed, such as academic journals, conferences, online courses, or community forums. Emphasize your commitment to continual learning and how you apply this knowledge in your work.

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What is your experience with cloud environments?

Emphasize your experience working with cloud environments like AWS, GCP, or Azure. You could share specific projects, the tools you used, and the advantages of utilizing cloud platforms in scaling machine learning applications.

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What techniques do you use to improve model inference speed?

Provide an overview of the specific techniques you use to enhance model inference speed, such as model quantization or distributed inference. Share examples of successful implementations where you've significantly improved performance.

Join Rise to see the full answer
Why do you want to work as a Machine Learning Engineer at Captions?

Express your passion for the video AI domain and how Captions' mission aligns with your professional goals. Highlight what excites you about the role and the potential impact you hope to make in shaping the future of video creation.

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EMPLOYMENT TYPE
Full-time, on-site
DATE POSTED
January 17, 2025

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