Technical Lead, Machine Learning

Own end-to-end ML system execution including data pipelines, training workflows, evaluation systems, inference architecture, and deployment. Fine-tune and adapt models using state-of-the-art methods such as LoRA, QLoRA, SFT, DPO, and distillation while architecting scalable inference systems that balance latency, cost, and reliability. Design and maintain data systems for high-quality synthetic and real-world training data, implementing evaluation pipelines covering performance, robustness, safety, and bias. Collaborate closely with application engineering to integrate ML systems cleanly into backend, mobile, and desktop products, making pragmatic trade-offs to ship improvements quickly under real production constraints. Translate research direction into reliable, scalable, production-grade ML systems with clear performance and quality targets.