Machine Learning Engineer, Applied AI

Lead applied AI initiatives by turning frontier generative models into production features across first-party apps and APIs. Design benchmarks, evaluation metrics, and robust datasets; perform error analysis and ensure numerical stability across data, training, and inference. Fine-tune and deploy models for text-to-image, image-to-text, image enhancement, editing, and multimodal use cases with measurable gains in quality, latency, or cost. Collaborate with product, research, engineering, and infra to ship reliable, scalable ML systems and define clear success criteria. Own 0-to-1 projects that shape Applied AI practices, contributing to safety, monitoring, and reliability. Requires 3+ years shipping ML products, backend Python/PyTorch/JAX coding, experience taking features from idea to production, and strong ownership in a startup setting.