SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Fal is building the generative media ecosystem and infrastructure powering the next generation of AI products. The company provides a unified platform combining high-performance inference, orchestration, and observability to help developers and enterprises move from idea to production at scale.
As a Software Engineer in Applied Machine Learning, 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:
- Develop novel model pipelines by extending state-of-the-art open-source image, video, audio, and 3D models with additional capabilities and modalities. Design novel approaches to model conditioning, generation, and editing using both training-free methods and fine-tuning.
- Build and maintain fine-tuning APIs that leverage Fal's GPU fleet, enabling customers to customize models for their specific needs.
- Identify common patterns across served models and develop reusable components, abstractions, and building blocks that accelerate development of new model capabilities and inference pipelines.
- Collaborate 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.
- Build, deploy, and maintain scalable, reliable generative model APIs. Anticipate and resolve production challenges to ensure reliable service at scale.
- Work directly with customers, including major e-commerce retailers and film/TV production studios, to develop novel solutions for their generative media needs.
Requirements:
- 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 for generative models.
- Demonstrated ability to independently take ambitious ML ideas from concept to production.
Nice-to-haves:
- Track record in fast-paced startup environments or digital media/entertainment industries.
