Own end-to-end ML system execution: 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.
Architect and operate scalable inference systems, balancing latency, cost, and reliability.
Design and maintain data systems for high-quality synthetic and real-world training data.
Implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership.
Own production deployment, including GPU optimization, memory efficiency, latency reduction, and scaling policies.
Collaborate closely with application engineering to integrate ML systems cleanly into backend, mobile, and desktop products.
Make pragmatic trade-offs and ship improvements quickly, learning from real usage.
Work under real production constraints: latency, cost, reliability, and safety

You have built or shipped real ML systems used by people, not just demos.
You are comfortable working with large models and understanding their failure modes.
You write strong, production-grade code and care about system correctness.
You are self-directed, pragmatic, and take full ownership of outcomes.
You communicate clearly and collaborate well in small, high-trust teams.

Technical Lead, Machine Learning

Key skills