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Research Engineer, MLOps

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 MLOps Research Engineer to architect and scale the machine learning infrastructure for our rapidly growing creative platform used by millions. You'll own the development of our distributed training systems, optimize our rapidly growing GPU clusters, and build performant inference pipelines that power our cutting-edge multimodal video diffusion models. As a key member of our ML Research team in a fast-growing Series C startup, you'll create foundational infrastructure enabling rapid research iteration while maintaining production-grade reliability and efficiency. We're already training large-scale models and are excited to dramatically expand our infrastructure capabilities.

Key Responsibilities

Core Systems Development:

  • Develop and optimize distributed training frameworks integrating multiple modalities (video, audio, text, and structured metadata)

  • Build flexible systems for cross-modal training orchestration and efficient experimentation

  • Design reproducible training environments with versioned dependencies and configurations

  • Implement comprehensive testing frameworks for validating model training correctness and performance

  • Create infrastructure for systematic model quality assessment and performance benchmarking

Infrastructure Development:

  • Design and implement flexible training orchestration systems that balance research agility with large-scale model training

  • Build robust monitoring and observability systems for complex training and inference pipelines

  • Design and manage GPU clusters optimized for distributed training of multimodal models

  • Build out comprehensive automated metrics collection and alerting across our ML stack

System Optimization:

  • Profile and optimize model training throughput using mixed precision, gradient checkpointing, and advanced memory techniques

  • Develop custom CUDA and Triton kernels to accelerate critical compute paths

  • Implement creative solutions for cost optimization across spot instances and reserved capacity

  • Design and optimize real-time inference systems enabling fast research iteration cycles

Research & Product Impact:

  • Build infrastructure enabling rapid testing of research hypotheses

  • Create systems supporting close collaboration between infrastructure and research teams

  • Develop frameworks for reproducible research experimentation

  • Enable seamless deployment of research innovations to production

Preferred Qualifications:

Technical Background:

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

  • Strong programming skills in Python and systems programming

  • Experience with distributed systems and scalable infrastructure

  • Track record of building reliable, performant large-scale ML systems

Areas of Expertise (Strong experience in some or all of these areas):

  • Deep expertise in PyTorch internals and distributed training frameworks (FSDP, DeepSpeed)

  • GPU cluster management and optimization

  • Performance profiling and systems optimization

  • CUDA programming and kernel optimization

  • Containerization and orchestration (Docker, Kubernetes)

  • ML model serving and deployment at scale

  • Language models and attention mechanism optimization

  • Video and audio processing pipelines

  • Large-scale diffusion models

Engineering Approach:

  • Love diving deep into complex systems optimization challenges

  • Take ownership of critical infrastructure while collaborating effectively

  • Get excited about pushing the boundaries of ML system performance

  • Want to work directly with researchers on cutting-edge ML problems

  • Thrive in fast-paced, research-driven environments

Team Culture

You'll work full-time, on-site in our NYC office alongside researchers and engineers who are dedicated to building world-class generative models and data infrastructures. We've intentionally built a culture that prizes open discussion of technical approaches, rapid iteration, and direct access to decision makers. Your success will be measured by the performance and reliability of our systems, enabling our researchers to iterate quickly on and develop ambitious ideas. You'll have significant autonomy to shape our infrastructure direction and direct impact on our ability to serve millions of creators.

Our team values:

  • Open technical discussions and collaboration

  • Rapid iteration and practical solutions

  • Deep technical expertise and continuous learning

  • Direct impact on research and product outcomes

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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Average salary estimate

$110000 / YEARLY (est.)
min
max
$90000K
$130000K

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What You Should Know About Research Engineer, MLOps, Captions

Are you an innovative mind looking to make your mark in the world of machine learning? Captions, the leading video AI company located in the heart of New York City, is on the hunt for a talented Research Engineer, MLOps to join our dynamic team. Here at Captions, we empower millions of creators and businesses with cutting-edge video solutions, and we’re rapidly expanding our reach. As a Research Engineer, MLOps, you’ll be at the forefront of developing and optimizing our machine learning infrastructure. This role is perfect for individuals excited about architecting scalable systems that support multimodal applications across video, audio, and text. You'll have the chance to design robust training frameworks, manage GPU clusters, and build performant inference pipelines. Collaborating closely with our passionate ML Research team, you will create foundational infrastructure that drives efficiency and reliability. The ideal candidate will have a strong technical background, thriving in a fast-paced environment where creativity meets rigorous engineering. If you're eager to push the boundaries of machine learning and be part of a supportive, knowledgeable team, Captions is the place for you! In addition to a vibrant work culture that values open communication and rapid iteration, we offer comprehensive benefits and significant growth opportunities. Join us in shaping the future of video creation!

Frequently Asked Questions (FAQs) for Research Engineer, MLOps Role at Captions
What are the responsibilities of a Research Engineer, MLOps at Captions?

As a Research Engineer, MLOps at Captions, your primary responsibilities will involve developing and optimizing our machine learning infrastructure, which includes architecting distributed training frameworks and managing GPU clusters for multimodal models. You'll create efficient systems for model quality assessment and performance benchmarking, working closely with the research team to enable rapid iteration cycles.

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What qualifications are required for the Research Engineer, MLOps position at Captions?

Candidates for the Research Engineer, MLOps role at Captions should have a Bachelor's or Master's degree in Computer Science, Machine Learning, or a related field. Strong programming skills in Python and experience with distributed systems is essential, along with a solid background in building reliable, scalable ML systems.

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What technical expertise does Captions look for in a Research Engineer, MLOps?

Captions seeks candidates with deep expertise in areas such as PyTorch internals, distributed training frameworks, GPU cluster optimization, and CUDA programming. Familiarity with containerization tools like Docker and orchestration platforms like Kubernetes is advantageous for this role.

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What is the team culture like for Research Engineers at Captions?

At Captions, the team culture is centered around open technical discussions, collaboration, and a commitment to rapid iteration. As a Research Engineer, you'll have the opportunity to work directly with researchers and engineers, contributing to significant projects while receiving support in your growth and learning.

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What benefits does Captions offer to its Research Engineers?

Captions provides a comprehensive benefits package that includes medical, dental, and vision plans, a 401K plan with employer match, and health and wellness perks. Additionally, team members enjoy catered lunches, dinner stipends for late work, and multiple offsite events throughout the year.

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Common Interview Questions for Research Engineer, MLOps
Can you explain your experience with distributed training frameworks like FSDP or DeepSpeed?

When answering this question, focus on specific projects where you've implemented these frameworks. Discuss how they improved your training processes, eased scalability, or optimized resource usage, showcasing your hands-on experience and problem-solving skills.

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How would you optimize GPU clusters for large-scale models?

Detail your approach to optimizing GPU clusters by discussing techniques such as load balancing, memory usage optimization, and job scheduling. Give examples of previous optimizations you've implemented and the tangible benefits they brought to your team or project.

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What methods do you use for profiling and optimizing model training throughput?

Explain your strategies for profiling, such as using metrics from tools like TensorBoard or custom scripts. Discuss your experiences with mixed precision training, gradient checkpointing, or memory management techniques to enhance performance.

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Describe a challenge you faced while working on machine learning systems and how you overcame it.

This is your opportunity to highlight your problem-solving skills. Be specific about the challenge, the strategies you employed to address it, and the overall impact on the system or project. Make sure to include the lessons learned.

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How do you ensure the reliability and efficiency of your ML systems?

Talk about your experience in implementing testing frameworks, monitoring systems, and continuously evaluating system performance. Share specific metrics or results that demonstrate the reliability and efficiency of your ML systems.

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What is your experience with model serving and deployment at scale?

Discuss your familiarity with deploying models in production environments, mentioning any frameworks or tools you've used, such as TensorFlow Serving or FastAPI. Highlight the lessons learned from scaling deployments and any trade-offs you had to manage.

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How do you approach collaboration between research and infrastructure teams?

Illustrate your understanding of the synergy between research and infrastructure by sharing examples of projects where you facilitated collaboration. Emphasize your communication skills and strategies for maintaining alignment between technical and research goals.

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What role does experimentation play in your engineering approach?

Discuss how you incorporate experimentation into your work, whether through A/B tests, rapid prototyping, or systematic research hypotheses testing. Provide an example of how experimentation led to improved outcomes in past projects.

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Can you describe your experience with MLOps tools or platforms?

Mention specific MLOps tools you have worked with, such as MLflow or Kubeflow, detailing your hands-on experience in managing the full machine learning lifecycle. Highlight how these tools helped improve your workflows and processes.

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How do you keep up with the latest trends in ML and MLOps?

Share your strategies for staying informed about industry advances, such as attending conferences, participating in online forums, or engaging with research papers. Mention any specific resources or networks that have significantly influenced your work.

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EMPLOYMENT TYPE
Full-time, on-site
DATE POSTED
January 10, 2025

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