Machine Learning Engineer, Applied AI

3+ years of building and shipping ML products. Write backend code in Python/ PyTorch/ Jax. Design benchmarks and evaluation metrics, run error analyses, and debug numerical stability across data, training, and inference. Taken ML features from idea to evaluation to production with measurable gains (quality, latency, or cost). Collaborate with the product team to define success criteria and iterate quickly and pragmatically. Comfortable curating, labeling, and cleaning datasets, and designing evaluation corpora and failure-mode analyses. Familiar with modern deep learning such as transformers, diffusion, and encoders. Bring ownership, urgency, curiosity, and learn fast in a startup setting.

Bridge research and product, turning generative models into production features. Experiment quickly, build rigorous evaluations and datasets. Partner with research, engineering, infra, and product to ship reliable and scalable ML systems. Fine-tune and deploy models for creative use cases (text-to-image, image-to-text, image enhancement and editing, multimodal). Set clear success metrics (quality, latency, cost). Contribute to safety, monitoring, and reliability of systems. Lead 0 to 1 projects that shape Applied AI practices. Deliver features that create delight and real value for users.