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fal is building the generative media ecosystem powering the next generation of AI products. The company provides infrastructure, tools, and model access that enable teams to move from idea to production at scale. fal serves developers and enterprises with a unified platform combining high-performance inference, orchestration, and observability for AI-native products.
You will join as an Applied Machine Learning Engineer with technical ownership of the model layer powering fal's next-generation generative media platform. This is a hands-on, production-focused role that bridges cutting-edge generative research with scalable, consumer-facing products.
Key responsibilities include:
• Novel Model Pipelines: Extend state-of-the-art open-source image, video, audio, and 3D models with additional capabilities and modalities. Develop novel approaches to model conditioning, generation, and editing using both training-free methods and fine-tuning.
• Fine-Tuning: Leverage fal's GPU fleet to fine-tune generative models for novel capabilities. Build and maintain fine-tuning APIs enabling customers to customize models for their specific needs.
• Architecture & Abstraction: Identify common patterns across served models and develop reusable components, abstractions, and building blocks that accelerate development of new model capabilities and inference pipelines.
• Inference Optimization: Partner with the ML Performance & Optimization team to apply state-of-the-art inference techniques, ensuring models run efficiently with low latency, high throughput, and optimal GPU utilization.
• Production Deployment: Build, deploy, and maintain scalable, reliable generative model APIs. Anticipate and resolve production challenges to ensure reliable service at scale.
• Customer Collaboration: Work directly with customers including major e-commerce retailers and film/TV production studios to develop novel generative media solutions.
You will split your time between extending SOTA models with additional capabilities and maintaining the fleet of generative model APIs.
REQUIREMENTS:
Required:
• 3+ years of professional experience as an Applied ML Engineer
• At least 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 (Hugging Face, Diffusers, related tooling)
• Strong engineering judgment designing scalable, reliable, secure, safe, and performant systems
• Demonstrated ability to independently take ambitious ML ideas from concept to production
Nice-to-have:
• Experience designing and implementing training-free extensions to image, video, audio, or 3D generative models (novel conditioning methods, inference-time modifications, new capabilities)
• Experience developing and executing custom post-training or fine-tuning approaches
• Track record in fast-paced startup environments or digital media and entertainment industries
