Experience

Professional Experience

Founder - Dan Gerlanc LLC - (01/2026 - Present)

• Fractional CTO/VP Engineering engagements helping companies build and scale engineering teams and set technical strategy.
• Host of the Agents and Engineers podcast

Co-founder and CTO - .txt - (10/2023 - 10/2025)

• Co-founded and raised $12M to build the first company focused on LLM structured generation, the building block of AI systems that produce clean, well-formatted data instead of raw text.
• Led development of the .txt API, licensable binary distribution, and AWS Marketplace deployable structured generation products.
• Personally hired or recruited the founding team, then scaled from 3 to 17 engineers within one year.

VP of Engineering - Normal Computing - (04/2023 - 10/2023)

• Led engineering at a deep-tech AI startup focused on production-ready, reliable, generative AI workflows.
• Scaled engineering team from 1 to 7 engineers in fewer than 6 months.
• Released Outlines, a leading open source library for structured generation (13K+ stars).

Senior Director - Data Science & ML Engineering - Ampersand - (10/2019 - 01/2023)

• Built, from the ground up, distributed Data Science and ML Engineering teams of 8 engineers.
• Co-authored technical blog post with AWS publicizing the end-to-end ML system my team built that served over 1.4 trillion forward-looking impression estimates with sub-second response-time using Python, FastAPI, and ClickHouse.
• Reduced average error of viewership forecasts by 40% using more than 200,000 fully Bayesian Hidden Markov Models, and released the core estimation framework, PyMC3 HMM, as Ampersand’s first open source project.

President & Founder - Enplus Advisors - (06/2011 - 10/2019)

• Founded data engineering and ML consultancy; delivered greenfield cloud data architectures and high-ingestion pipelines across Fortune 500 and growth-stage clients; identified $60M+ in revenue opportunities
• Energy, IoT & telemetry: Built infrastructure for LinkCycle, Sense, Climate Central, and Flo by Moen; architected AWS + PostgreSQL + InfluxDB pipelines for high-throughput sensor telemetry (10M+ data points/day per dozen devices at Flo)
• Data platforms: Migrated Passiv.AI quantitative analytics to AWS Redshift ELT over hundreds of millions of records (3,000%+ universe expansion); designed PostgreSQL-backed pipeline for K–12 SaaS processing 7.4M survey responses

Quantitative Analyst - Geode Capital Management - (09/2007 - 04/2011)

• Built ML models and automated data pipelines for signal generation, alpha research, and portfolio construction supporting multi-billion-dollar quantitative equity strategies
• Passed CFA Level 1 and Level 2 exams

Teaching Experience

O’Reilly Media Instructor

Taught thousands of students across multiple courses and formats, maintaining consistently high student satisfaction ratings.

Course content:

• Python programming
• Pandas for data analysis
• Dask for parallel computing
• Machine learning engineering

Ratings:

• Top instructor ratings based on 104 student feedback responses

Teaching approach:

• Focus on practical, hands-on learning
• Real-world examples from production systems
• Clear explanations of complex technical concepts

Publications

Jeffrey Enos, Daniel Gerlanc, Brandon Willard, Pierre-Yves Aquilanti, and Ala Abunijem. Bayesian ML Models at Scale with AWS Batch. AWS HPC Blog, June 14, 2022.

Kirby, K. N., & Gerlanc, D. (2017). Finding Bootstrap Confidence Intervals for Effect Sizes with BootES. APS Observer, 30(3).

Iyengar A, Paulus JK, Gerlanc DJ, Maron JL. Detection and Potential Utility of C-Reactive Protein (CRP) in Saliva of Neonates. Frontiers in Pediatrics, November 2014.

Daniel Gerlanc and Kris Kirby, bootES: An R Package for Bootstrap Confidence Intervals on Effect Sizes. Behavioral Research Methods, March 2013. (Preprint)

Kyle Campbell, Jeff Enos, Daniel Gerlanc, and David Kane. Backtests. R News, 7(1):36-41, April 2007.

Talks & Podcasts

Hosting

• Agents and Engineers — Agents and Engineers is a podcast about agentic AI and software engineering. Each episode is a conversation with the practitioners building with agents and the people crafting the tools that make them work. Listen on Apple Podcasts, Spotify, or YouTube.

Guest appearances

• Shaping the Accuracy of LLM-Generated Content with Outlines, ODSC AI X Podcast, April 2024. Audio.
• Evaluating New Open Source Tech Panel, Talk Python To Me. Audio.
• Bayesian models and half a million cores - what’re you waiting for?, AWS HPC Tech Shorts, June 2022.
• The Truth Behind the Data with Data Scientist Dan Gerlanc, solo.coder Podcast, July 2019.
• Data Science Consulting, The Accidental Engineer, January 2018.

Invited Talks

• Round Trip Client-Side COPY for High Volume Postgres Inserts, PGConf 2017 NYC, November 2017. Slides.
• Introduction to R, Open Data Science Conference, June 2015. Slides.
• Open Source Finance with R, Boston Data Mining, December 2013. Slides.
• Hands on Machine Learning, Boston Predictive Analytics Machine Learning Workshop, December 2012. Slides.
• Introduction to Data Science and Machine Learning, General Assembly Boston, July 2013.
• Random Forests Lightning Talk, Predictive Analytics World, October 2012.
• Predicting Customer Conversion with Random Forests, New England AI Meetup, October 2012. Slides.

Open Source

gig: A CLI utility that generates .gitignore files from GitHub’s template collection, with templates embedded directly into the binary at compile time.

mmi (Mother May I?): A CLI utility that acts as a PreToolUse Hook for Claude Code, providing intelligent auto-approval of safe Bash commands.

bootES: Calculate robust measures of effect sizes using the bootstrap.

backtest: The backtest package provides facilities for exploring portfolio-based conjectures about financial instruments (stocks, bonds, swaps, options, et cetera).

portfolio: Classes for analysing and implementing equity portfolios, including routines for generating tradelists and calculating exposures to user-specified risk factors.

Programming with Data: Go from beginner to practitioner using Python and pandas to manipulate tabular data. Taught to 1,000s of students around the work and assumes no experience with pandas.

Education

Williams College

Degree: Bachelor of Arts, Comparative Literature

Created a de-facto data science major through coursework in:

• Computer Science
• Economics
• Statistics