- Set technical strategy for a broad area of the ML roadmap, translating ambiguous business and research goals into scoped, production-ready systems.
- Tackle complex modeling problems such as long-context understanding, multi-document analysis, and complex reasoning.
- Apply advanced ML techniques like fine-tuning, reinforcement learning, and retrieval while balancing innovation with practical system constraints.
- Establish rigorous evaluation standards to reduce hallucinations, improve factual consistency, and define quality benchmarks.
- Drive data excellence by performing hands-on analysis of training and evaluation data to manage noise and drift at scale.
- Provide technical leadership and mentorship to the ML team, raising the bar for experimentation and engineering rigor.
- Act as a bridge between research and production, ensuring new techniques are successfully integrated into shippable products.
- Partner cross-functionally with product, engineering, and legal subject-matter experts to define technical direction.
- Scale machine learning systems cost-effectively while ensuring they remain grounded in business and customer needs.
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