Software Engineer, Applied Machine Learning
$180k - $230k • Remote • San Francisco • FullTime
Posted 6h ago
Job Location
San Francisco
Tech Stack
Remote Work Policy
Fully remote
Employment Type
FullTime
Categories
Applied AI Engineer
About the job
fal is building the generative media ecosystem for the next generation of AI products, providing the infrastructure, tools, and model access needed to move from idea to production at scale. We are seeking a hands-on, production-focused Applied Machine Learning Engineer to take technical ownership of the model layer powering our next-generation generative media platform. In this role, you will bridge the gap between cutting-edge generative research and scalable, consumer-facing products, splitting your time between extending state-of-the-art open-source models and maintaining our fleet of generative model APIs.
Responsibilities
- Extend state-of-the-art image, video, audio, and 3D models with additional capabilities and modalities, developing novel approaches to model conditioning, generation, and editing.
- Fine-tune generative models for novel capabilities using a large GPU fleet and build/maintain fine-tuning APIs for customer customization.
- Identify common patterns across models and develop reusable components, abstractions, and building blocks to accelerate new model capability development and inference pipelines.
- Apply state-of-the-art inference techniques and best practices to ensure efficient model performance with low latency, high throughput, and optimal GPU utilization.
- Build, deploy, and maintain scalable, reliable generative model APIs, anticipating and resolving production challenges.
- Collaborate directly with customers, including major retailers and film studios, to develop novel generative media solutions.
Requirements
- 3+ years of professional experience as an Applied ML Engineer, with 1-2 years focused on generative media or computer vision.
- Expert-level proficiency in Python and PyTorch.
- Deep practical understanding of diffusion and flow-based generative models.
- Hands-on experience with open-weight model ecosystems like Hugging Face and Diffusers.
- Ability to anticipate and solve challenges in deploying ML models to production, with strong engineering judgment for scalable, reliable, secure, safe, and performant systems.
- Experience designing and implementing training-free extensions to generative models.
- Experience developing and executing custom post-training or fine-tuning approaches for generative models.
- Demonstrated ability to independently take ambitious ML ideas from concept to production.
Benefits
- Health, dental, and vision insurance (US)
- Regular team events and offsites