Databricks
Principal Forward Deployed AI Engineer · Contract
Partner with enterprise teams to design and deliver full-stack agentic and generative-AI systems on the Databricks platform.
2026—Now
AI researcher · Machine learning engineer
McClain Thiel
I build production AI systems and research generative models, post-training, and evaluation. I'm also a PhD student in the Barnes Lab at UCL, where I apply these methods to biology.
01 / Experience
Research to production
I have worked across the AI lifecycle—from research and model development to enterprise deployment—within data infrastructure, foundation-model tooling, and healthcare.
2025—2029
University College London
PhD Student · Barnes Lab · Systems Biology, part-time
Researching language-model post-training and reinforcement learning for biological sequence design.
2025—Now
Encode: AI for Science
AI for Science Fellow · Pillar VC, ARIA & DSIT
Selected for a fellowship supporting researchers applying frontier AI methods to ambitious scientific problems.
2024—2025
Snorkel AI
Senior Machine Learning Engineer
Led work across synthetic data, multimodal evaluation, and agentic data generation to improve task-specific performance of generative-AI systems.
Acquired 2025
WEALTHAWK
Founder & CTO
Built an AI-powered platform that identifies money-in-motion events for financial advisors. Acquired by Praxis Solutions.
2022—2024
Tempus
Machine Learning Scientist · AI Solution Architect
Founding member of the generative-AI team. Led the company's first external LLM application, adopted by hundreds of pharma users, and developed secure clinical and genomic AI systems.
2021—2022
Nference
Data Scientist
Developed probabilistic models and clinical ML algorithms, while establishing shared MLOps and experiment-tracking practices.
2020
UC Berkeley
B.A. Data Science · Minor in Bioengineering
Studied machine learning, statistics, and biological systems.
02 / Research
Publications
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2026 ICML
Effects of Structural Reward Shaping on Biophysical Properties in RL-Trained Plasmid Generators
Read paper ↗ -
2026 NeurIPS · In review
PlasmidLM: A Promptable DNA Language Model via Verifiable-Reward Post-Training
Read preprint ↗ -
2025 In review
Generative Design and Construction of Functional Plasmids with a DNA Language Model
Read preprint ↗ -
2024 In review
Designing Minimal E. coli Genomes Using Variational Autoencoders
Read preprint ↗ -
2020 AGU Fall Meeting
Crop Stage Estimation: A Multi-Satellite Historical Model and a Scalable Neural Network Forecaster
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2020 Berkeley Institute for Data Science
Generative Retraining of Rare Images for Computer Vision Systems
03 / Speaking & writing
Sharing the work
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2026 · Slides ↗
Introduction to AI for Life Scientists
Workshop for early-stage life-science founders · Nucleate UK
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Feb 2026 ↗
Reinforcement Learning and Genomic Language Models
In Silico #004 · Phoenix Court, London
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Essay ↗
Computational Biology's ChatGPT Moment
On making genomic foundation models useful beyond ML labs.
04 / Contact
Consulting & collaboration
Practical AI expertise, from strategy through delivery.
Available for selected consulting, technical advisory work, and academic collaborations in generative AI, agentic systems, evaluation, and AI for science.
me [at] mcclainthiel.com