Alnur Ali Email & Phone Number

San Francisco, CA, US

Leading projects applying large language models / generative AI to enterprise data, at Databricks

Greenwich, CT, US

I advise / consult for various machine learning startups (typically lying within the span of my social network). I love helping people think through (technical) problems – so if you’d like to chat, please feel free to reach out!

San Francisco, California, US

• (Sisu was an a16z-backed analytics startup, which was acquired by Snowflake)
• Managed teams of 10-20 research scientists, engineers, product managers, and designers working on (i) large language models / generative AI; (ii) time series analysis; (iii) segmentation analysis; and (iv) causal.
• My team also helped build out our machine learning infrastructure (using Ray) — leading to a 10x increase in the number of models shipped
• Helped define the overall company strategy, engineering roadmap, etc. Helped close several hundred million dollar deals.
• Technologies: cloud data warehouses (Redshift, BigQuery, Snowflake, …), cloud storage, Parquet, Arrow, React, Flask, Ray, Kubernetes, Python, Rust

Stanford, CA, US

• Worked on "responsible AI" / "AI safety" / reliably and safely deploying ML models
• Built algorithms that (i) track deployed model performance, raise an alarm when performance degrades, and generate interpretable diagnostics; (ii) repair and protect any deployed models from future degradations in.
• Mentored 5 senior Ph.D. students
• (Some) technical keywords: distribution shift, robust optimization, subpopulation-level performance, conformal inference / distribution-free uncertainty quantification, weak supervision, tuning parameter-free.
• Application areas: finance, operations research, public policy, epidemiology

• Developed and productionized high-risk research ideas on ML for web search
• Mentored 5 research interns
• Technical keywords: personalized click prediction, localized ranking for better subpopulation-level performance, "early exits" for faster gradient-boosted decision trees

Redmond, Washington, US

• Worked on ranking and query classification / reformulation / rewriting
• Led teams of 5-10 engineers and research scientists to (i) build and ship models; (ii) do feature engineering; (iii) mine terabytes of user behavior logs; (iv) run A/B tests — leading to (roughly) 20% improvements in.
• Significant experience (i) defining product strategy and engineering roadmap; (ii) working cross-functionally to get things done; (iii) building high-performing teams; (iv) evaluating acquisitions
• Tech transfer to bring research ML into the enterprise (Sharepoint, Office, Dynamics 365, Power BI)
• Responsible for a number of 1sts: (i) led the 1st integration of statistical machine translation into a commercial ranking system; (ii) built the Bing team’s 1st active learning pipeline; (iii) built Sharepoint’s 1st.
• (Some) technical keywords: gradient-boosted decision trees, neural nets, contextual bandits, off-policy evaluation, robust ranking models, random walk-type representations, natural language processing, sequence models.

Postdoctoral Scholar, Departments Of Statistics And Electrical Engineering

Doctor Of Philosophy - Phd, Machine Learning

Bachelor Of Science - Bs, Computer Science