Mirage is redefining short-form video with frontier AI research.
We’re building full-stack foundation models and products that are changing the future of this format and video creation, production and editing more broadly. Over 20 million creators and businesses use Mirage’s products to reach their full creative and commercial potential.
We are a rapidly growing team of ambitious, experienced, and devoted engineers, researchers, designers, marketers, and operators based in NYC. As an early member of our team, you’ll have an opportunity to have an outsized impact on our products and our company’s culture.

REQUIREMENTS:

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.

ABOUT THE ROLE AND TEAM:

Captions seeks an exceptional Research Engineer (MOTS) 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.
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.

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.