We’re seeking a high-agency Forward Deployed / Applied ML Engineer to bridge cutting-edge generative AI research with real-world production systems. You’ll work directly with customers to deploy, optimize, and customize our FLUX diffusion models across diverse environments, from on-prem GPU clusters to hosted infrastructure.
Key Responsibilities
Deploy and optimize FLUX diffusion models in customer environments, balancing latency, cost, and output quality
Architect deep product integrations beyond APIs, including model hosting, inference optimization, and production deployment Fine-tune and customize foundation models for customer-specific visual media use cases Lead technical deep dives with customers to diagnose model, infrastructure, and performance issues Translate customer challenges into actionable engineering solutions and research feedback Identify emerging industry use cases for generative visual AI
Required Qualifications
Hands-on experience deploying and serving generative AI / deep learning models in production Strong expertise in diffusion models, model fine-tuning, optimization, and inference Proven experience as an ML Engineer shipping models used by real systems Strong Python skills and experience designing and consuming APIs Ability to communicate complex ML tradeoffs to both technical and non-technical stakeholders Experience working directly with customers on technical AI integrations Know the FLUX ecosystem intimately—ComfyUI, common training frameworks, the tools practitioners actually use
Preferred Qualifications
Deep knowledge of diffusion models, flow matching, distillation, and advanced fine-tuning techniques Experience optimizing inference for transformer-based models under real production constraints Experience deploying models on cloud platforms with modern serving infrastructure Background contributing to open-source ML / diffusion model projects Experience designing solutions in constrained enterprise environments
