Research Engineer, Generative Video
At a glance
Mid-level AI Research role at Mirage. New York · full-time · $175,000–$300,000 base.
aimlroles summary, based on the employer's posting.
What you'll do
- Train video and multimodal models at scale, then tune them for stronger efficiency
- Reduce training and inference demands across memory use, response time and operating cost
- Speed up diffusion and autoregressive generation with distillation, quantization and pruning
- Build distributed training infrastructure and improve GPU use, parallel execution and throughput
- Create experiment and debugging tools, then move research models into production and track their performance
What you bring
- At least two years of professional industry experience
- A bachelor's, master's or doctorate in computer science, machine learning or a related area
- Strong background in deep-learning systems and infrastructure, including PyTorch, CUDA, Triton and distributed training
- Experience scaling large models for low-latency inference, with strong debugging and performance-profiling skills
Who this fits
This role suits an engineer who can work across machine-learning research and production systems, moving prototypes into deployable models. The position is based in New York, and most roles require in-person work at the NYC headquarters.
From the employer
Mirage is an AI video company focused on making creation dramatically easier. Our team tackles some of the hardest creative and technical challenges in generative media.
We get to rethink how people collaborate with AI that can actually do creative work with them—defining entirely new interaction paradigms for AI-native video tools. We make that possible by building multimodal agents that turn creative direction into edits, rendering engines that composite multiple layers of video and graphics, and generative video models trained from the ground up that create new media when needed.
All of this comes together in Captions, our creative workspace for generating, editing, and designing video. The technology behind Captions is now available more broadly: Tesseract gives any AI agent native video-editing capabilities, while developers can integrate our models into their own products through APIs.
Explore our work
Captions — Our flagship creative workspace
Tesseract — Our professional video engine for AI agents
Research — The foundation models we build in-house
Updates — The latest from Mirage
TechCrunch, Forbes, Fast Company — Press
Our Investors
We’re very fortunate to have some the best investors and entrepreneurs backing us, including Index Ventures, Kleiner Perkins, Sequoia Capital, Andreessen Horowitz, General Catalyst, Uncommon Projects, Kevin Systrom, Mike Krieger, Lenny Rachitsky, Antoine Martin, Julie Zhuo, Ben Rubin, Jaren Glover, SVAngel, 20VC, Ludlow Ventures, Chapter One, and more.
Please note that most of our roles will require you to be in-person at our NYC HQ (located in Union Square)
About the Role
Mirage is seeking an ML Engineer to build and scale the systems powering our video generation models. You’ll work on novel modeling approaches, training objectives, scaling strategies, and inference optimization and efficiency to bring cutting-edge models into production.
This role sits at the intersection of research and systems engineering, focusing on making advanced models faster, more efficient, and capable of ultra-low latency, real-time generation.
Responsibilities
Train and optimize large-scale video and multimodal models
Improve efficiency across training and inference (memory, latency, cost)
Implement techniques such as distillation, quantization, and pruning to aggressively accelerate diffusion and autoregressive generation
Build and maintain distributed training systems
Optimize GPU utilization, parallelism, and throughput
Develop tooling for experimentation, evaluation, and debugging
Translate research models into robust, production-ready systems
Monitor and improve model performance in real-world usage
What makes you a great fit
BS/MS/PhD in CS, ML, or related field
2+ years of professional industry experience
Strong experience in deep learning systems and infrastructure
Expertise in PyTorch, CUDA, Triton, and distributed training (FSDP, etc.)
Experience scaling and optimizing large models under low-latency inference constraints
Strong debugging and performance profiling skills
Ability to move quickly from prototype to production
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!
Grubhub subscription
Health & Wellness Perks
Multiple team offsites per year with team events every month
Generous PTO policy
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.