Software Engineer, Applied Machine Learning

fal•San Francisco, CA

1d•$180,000 - $230,000

About The Position

fal is seeking a hands-on, production-focused Applied Machine Learning Engineer to take technical ownership of the model layer powering its generative media platform. The role bridges generative research and scalable, consumer-facing products, extending state-of-the-art open-source models with new capabilities and helping maintain generative model APIs. Work spans image, video, audio, and 3D models, including model conditioning, generation, editing, training-free extensions, and fine-tuning. The position also involves building reusable model and inference components, optimizing performance, deploying reliable APIs, and collaborating directly with customers on generative media solutions.

Requirements

At least 3 years of professional experience as an Applied ML Engineer, including 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, including Hugging Face, Diffusers, and related tooling.
Ability to anticipate and solve challenges involved in deploying machine learning models to production.
Strong engineering judgment in designing scalable, reliable, secure, safe, and performant systems.
Demonstrated ability to independently take ambitious machine learning ideas from concept to production.

Nice To Haves

Experience designing and implementing training-free extensions to image, video, audio, or 3D generative models, such as novel conditioning methods, inference-time modifications, or new model capabilities.
Experience developing and executing custom post-training or fine-tuning approaches to extend generative models.
A track record of working in fast-paced startup environments or in digital media and entertainment industries.

Responsibilities

Work with the post-training team to extend state-of-the-art image, video, audio, and 3D models with additional capabilities and modalities.
Develop approaches to model conditioning, generation, and editing, including training-free methods and model fine-tuning.
Fine-tune generative models using the company’s GPU fleet and build and maintain fine-tuning APIs for customer customization.
Identify common patterns across served models and develop reusable components, abstractions, and building blocks for model capabilities and inference pipelines.
Collaborate with the ML Performance & Optimization team to apply inference techniques that improve latency, throughput, and GPU utilization.
Build, deploy, and maintain scalable, reliable generative model APIs, and address production challenges.
Work directly with customers to develop generative media solutions for their needs.