Michael Tontchev
Staff Software Engineering Tech Lead at Meta
Seattle, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolMichael Tontchev is a Staff Software Engineering Tech Lead at Meta with 11 years of experience building scalable systems and leading teams across AI and infrastructure domains. Based in Seattle, he has progressed from software engineering roles at Microsoft to senior technical leadership at Meta, where he focuses on AI safety and alignment for frontier models. His hands-on contributions span TypeScript tooling and ML data pipelines—maintaining ts-essentials types and developing fine-tuning data formatters for the popular meta-llama cookbook—reflecting a blend of rigorous engineering and applied ML. Known for pragmatic fixes (like DeepReadonly edge cases) and production-oriented testing, he pairs strong technical craft with an interest in ensuring AI aligns with human values.
11 years of coding experience
12 years of employment as a software developer
High School Accelerated math and science, High School Accelerated math and science at Baltimore Polytechnic InstituteBachelor's degree Computer Science and Economics, Bachelor's degree Computer Science and Economics at University of Maryland
Bulgarian, Italian, English
Stackoverflow
979reputation
178kreached
24answers
28questions
Github Skills (24)
data-formats10
pytorch10
typesc10
python10
testing10
llama10
machine-learning10
typescript10
data-format10
llm10
type-system10
ai10
typescript-types10
fine-tuning10
typescripts10
Programming languages (6)
Github contributions (5)
meta-llama/llama-cookbook
Dec 2023 - Dec 2023
Welcome to the Llama Cookbook! This is your go to guide for Building with Llama: Getting started with Inference, Fine-Tuning, RAG. We also show you how to solve end to end problems using Llama model family and using them on various provider services
Role in this project:
ML Engineer
ML EngineerContributions:5 reviews, 1 PR, 12 comments in 13 days
Contributions summary:Michael primarily focused on developing and refining data formatting and processing utilities for fine-tuning Llama Guard models within the repository. Their contributions included creating formatters and tests, along with implementing data augmentation strategies. These changes were centered around the `finetuning_data_formatter.py` file, indicating a strong emphasis on preparing and transforming data for machine learning tasks. The user also made minor updates to testing and documentation related to the data formatter.
fine-tuninggoinferencellamaai
ts-essentials/ts-essentials
Feb 2019 - Feb 2019
All essential TypeScript types in one place 🤙
Role in this project:Backend Developer
Backend DeveloperContributions:7 commits, 1 PR, 6 comments in 3 days
Contributions summary:Michael primarily focused on maintaining and improving the `ts-essentials` library. Their contributions include adding a check for `ReadonlyArray<T>` within the `DeepReadonly` type to fix a bug and adding a build-time test to ensure the fix remains. The user also performed several styling fixes across test files. The commits demonstrate a focus on TypeScript type definitions and testing.
typescript-typestypescripttype-level-programmingtoolbox
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