At a glanceSummarised by Seekless from the posting.
Must have7
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 working with open-weight model ecosystems, including Hugging Face, Diffusers, and related tooling
Ability to anticipate and solve challenges that arise when deploying ML models to production
Strong engineering judgment in designing systems that are scalable, reliable, secure, safe, and performant
Nice to have4
Experience designing and implementing training-free extensions to image, video, audio, or 3D generative models
Experience developing and executing custom post-training or fine-tuning approaches to extend generative models with additional capabilities
Track record of working in fast-paced startup environments or digital media and entertainment industries
Demonstrated ability to independently take ambitious ML ideas from concept to production
Skills
Python
PyTorch
Hugging Face
Diffusers
Benefits
Health insurance
Dental insurance
Vision insurance
About the company
fal is the generative media ecosystem powering the next generation of AI products. We build the infrastructure, tools, and model access that teams need to move from idea to production, and do it at scale without compromise. For developers and enterprises, fal is the foundation that makes generative media not just possible, but practical: a unified platform where high-performance inference, orchestration, and observability come together to unlock new categories of AI-native products.
As generative media reshapes industries across a market projected to grow by hundreds of billions over the next decade, fal is becoming the ecosystem that ambitious teams build on.
About this role:
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.
Responsibilities
You will split your time between extending SOTA open-source models with additional capabilities and helping maintain our fleet of generative model APIs.
What you’ll do:
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Novel Model Pipelines: Work with our post-training team to extend SOTA image, video, audio, and 3D models with additional capabilities and modalities. Develop novel approaches to model conditioning, generation, and editing, including both training-free methods and model fine-tuning.
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Fine-Tuning: Leverage our massive GPU fleet to fine-tune generative models for novel capabilities. Build and maintain fine-tuning APIs that allow customers to customize models for their specific needs.
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Architecture & Abstraction: Identify common patterns across the models we serve and develop reusable components, abstractions, and building blocks that accelerate the development of new model capabilities and inference pipelines.
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Inference Optimization: Work hand-in-hand with our ML Performance & Optimization team to apply state-of-the-art inference techniques and best practices, ensuring models run efficiently with low latency, high throughput, and optimal GPU utilization.
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Production Deployment: Build, deploy, and maintain scalable, reliable generative model APIs. Anticipate and resolve production challenges to ensure our models serve customers reliably at scale.
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Customer Collaboration: Work directly with customers, including some of the world’s largest e-commerce retailers and film and TV production studios, to develop novel solutions to their generative media needs.
Qualifications/Nice-to-haves:
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Experience: 3+ years of professional experience as an Applied ML Engineer, with at least 1–2 years focused on generative media or computer vision.
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Core Frameworks: Expert-level proficiency in Python and PyTorch.
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Generative Media: Deep practical understanding of diffusion and flow-based generative models.
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Open-Source Tooling: Hands-on experience working with open-weight model ecosystems, including Hugging Face, Diffusers, and related tooling.
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Engineering Rigor: Ability to anticipate and solve challenges that arise when deploying ML models to production. Strong engineering judgment in designing systems that are scalable, reliable, secure, safe, and performant.
Nice to have
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Training-Free Model Extensions: 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.
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Model Post-Training: Experience developing and executing custom post-training or fine-tuning approaches to extend generative models with additional capabilities.
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Startup Experience: Track record of working in fast-paced startup environments or digital media and entertainment industries.
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Ability to Ship: Demonstrated ability to independently take ambitious ML ideas from concept to production.
What we offer at fal:
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Interesting and challenging work
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A lot of learning and growth opportunities
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Health, dental, and vision insurance (US)
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Regular team events and offsites
U.S. EQUAL EMPLOYMENT OPPORTUNITY INFORMATION:
fal provides equal employment opportunities to applicants and employees without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, or any other classification protected by applicable law.