Machine Learning Engineer

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Captions

company description

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 of 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 do not work with third-party recruiting agencies, please do not contact us.


about the role

Captions is seeking a Machine Learning Engineer to partner closely with our Researchers and bring large-scale multimodal video diffusion models into production. You’ll be responsible for optimizing and deploying state-of-the-art generative models (tens to hundreds of billions of parameters) to deliver low-latency, high-throughput inference at scale. This is a unique opportunity to work on cutting-edge AI—spanning audio-video generation, diffusion architectures, and temporal modeling—and ensure these innovations reach millions of creators worldwide.


responsibilities

inference and deployment

  • Develop high-performance GPU-based inference pipelines for large multimodal diffusion models
  • Build, optimize, and maintain serving infrastructure to deliver low-latency predictions at large scale
  • Collaborate with DevOps teams to containerize models, manage autoscaling, and ensure uptime SLAs

model optimization and fine-tuning

  • Leverage techniques like quantization, pruning, and distillation to reduce latency and memory footprint without compromising quality
  • Implement continuous fine-tuning workflows to adapt models based on real-world data and feedback

production mlops

  • Design and maintain automated CI/CD pipelines for model deployment, versioning, and rollback
  • Implement robust monitoring (latency, throughput, concept drift) and alerting for critical production systems

performance and scaling

  • Explore cutting-edge GPU acceleration frameworks (e.g., TensorRT, Triton, TorchServe) to continuously improve throughput and reduce costs

requirements

technical expertise

  • Proven experience deploying deep learning models on GPU-based infrastructure (NVIDIA GPUs, CUDA, TensorRT, etc.)
  • Strong knowledge of containerization (Docker, Kubernetes) and microservice architectures for ML model serving
  • Proficiency with Python and at least one deep learning framework (PyTorch, TensorFlow)

model optimization

  • Familiarity with compression techniques (quantization, pruning, distillation) for large-scale models
  • Experience profiling and optimizing model inference (batching, concurrency, hardware utilization)

infrastructure

  • Hands-on experience with ML pipeline orchestration (Airflow, Kubeflow, Argo) and automated CI/CD for ML
  • Strong grasp of logging, monitoring, and alerting tools (Prometheus, Grafana, etc.) in distributed systems

domain experience

  • Exposure to diffusion models, multimodal video generation, or large-scale generative architectures
  • Experience with distributed training frameworks (FSDP, DeepSpeed, Megatron-LM) or HPC environments

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 and wellness perks (Talkspace, Kindbody, One Medical subscription, HealthAdvocate, Teladoc)
  • 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.

Location

    Union Square, New York City

Job type

  • Fulltime

Role

Engineering

Keywords

  • Engineering
  • Full-time
  • gpu inference