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Salary: USD 180,000 - 230,000 / annual
Fal is a generative media infrastructure platform that powers the next generation of AI products. The company provides developers and enterprises with high-performance inference, orchestration, and observability tools to move generative media applications from idea to production at scale.
In this role, you will take technical ownership of the model layer powering Fal's next-generation generative media platform, bridging cutting-edge generative research with scalable, consumer-facing products.
Key responsibilities include:
- Extending state-of-the-art open-source models (image, video, audio, 3D) with additional capabilities and modalities, developing novel approaches to model conditioning, generation, and editing through both training-free methods and fine-tuning.
- Leveraging Fal's GPU fleet to fine-tune generative models for novel capabilities and building fine-tuning APIs that allow customers to customize models for their specific needs.
- Identifying common patterns across served models and developing reusable components, abstractions, and building blocks to accelerate new model capability development.
- Collaborating 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.
- Building, deploying, and maintaining scalable, reliable generative model APIs, anticipating and resolving production challenges.
- Working 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 and maintaining the fleet of generative model APIs.
Qualifications:
- 3+ years of professional experience as an Applied ML Engineer, with 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 in designing scalable, reliable, secure, and performant ML systems for production.
- Experience designing and implementing training-free extensions to generative models (novel conditioning, inference-time modifications, new capabilities).
- Experience developing custom post-training or fine-tuning approaches to extend generative models.
- Demonstrated ability to independently take ambitious ML ideas from concept to production.
Nice-to-haves: startup experience or background in digital media and entertainment industries.
