Software Engineer, Applied Machine Learning

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Software Engineer, Applied Machine Learning in USA new

fal
Full time
Email

USA
11/11/2026
Report

San Francisco, CA

Responsibilities

Extend image, video, audio, and 3D generative models with new capabilities and modalities using training-free methods and fine-tuning.
Develop and maintain fine-tuning APIs that allow customers to customize generative models.
Create reusable model abstractions, components, and inference building blocks.
Apply inference optimization techniques to improve latency, throughput, and GPU utilization.
Build, deploy, and maintain scalable and reliable generative model APIs.
Collaborate directly with customers to develop generative media solutions for their needs.

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 and Diffusers.
Experience designing training-free extensions and custom post-training or fine-tuning approaches for generative models.
Ability to solve production deployment challenges and design scalable, reliable, secure, safe, and performant systems.
Track record in fast-paced startup environments or digital media and entertainment industries is a nice-to-have.
Demonstrated ability to independently take ambitious machine learning ideas from concept to production.

Benefits

Health, dental, and vision insurance for US employees.
Regular team events and offsites.
Interesting and challenging work with learning and growth opportunities.

Salary: $180k - $230k/yr

fal

fal is a generative media platform that provides developers with access to the world's best generative image, video, and audio models through a unified API. Trusted by over 2.5 million developers and leading companies, fal offers the fastest inference engine for diffusion models, on-demand server...

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