Member of Technical Staff

Thesis (YC F25); San Francisco (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Thesis in San Francisco is seeking a highly capable Member of Technical Staff to build an autonomous AI research system. You will work on frontier evolutionary search, large-scale GPU orchestration, and end-to-end AI pipelines to accelerate discovery.

You should have a strong ML foundation, graduate degree preferred, and experience with distributed systems, Python, and Rust. You will ship robust experiments at scale and contribute to AI for science across biology, chemistry, and physics.

Qualifications

• Deep foundations in ML or related quantitative field.
• Experience with automated discovery methods is a plus.
• Strong math/statistics background preferred.

Responsibilities

• Autonomous AI R&D: Design an end-to-end AI system for automated hypothesis generation and model training.
• Evolutionary Search: Build algorithms that navigate large combinatorial spaces.
• Massively Parallel Systems: Build distributed GPU infrastructure for thousands of concurrent experiments.
• AI for Science: Apply AI research to biology, chemistry, and physics challenges.

Skills

Evolutionary Search

Reinforcement Learning

Systems Architecture

Applied Science

Python

Rust

Education

Graduate degree in ML/CS/Math/Stats/Physics

Job description

The Mission

Thesis is assembling a Manhattan Project for autonomous AI research.

We are building the final AI model: a machine to build all other models. Rather than manually chasing isolated model breakthroughs, the hill-climbing machine automates AI R&D itself. By pointing it at grand challenges, we intend to invent the next Transformer or next AlphaFold breakthrough.

As a Member of Technical Staff, you will build this autonomous AI research system to usher in the golden era of discovery. Practically, this includes working on frontier evolutionary search algorithms, agent loops, parallel GPU infrastructure, and fine-tuning.

What You’ll Work On

• Autonomous AI R&D: Design an end-to-end AI system for automated hypothesis generation and model training.
• Evolutionary Search: Build algorithms that navigate massive combinatorial spaces.
• Massively Parallel Systems: Build the distributed infrastructure required to orchestrate thousands of concurrent GPU experiments.
• AI for Science: Direct Darwin’s search capacity toward frontier biological, chemical, and physical challenges.
  
  

Who You Are

We seek exceptional AI researchers and engineers to try the unthinkable and seemingly improbable.

• Research Rigor: Deep foundations in ML. A graduate degree in Machine Learning, CS, Math, Stats, Physics, or related quantitative field, or an equivalent track record, is strongly preferred.
• Core Expertise in One or More Areas
• Evolutionary Search & RL: Evolutionary algorithms, meta-learning, reinforcement learning, or related methods for automated discovery.
• Systems Architecture: Distributed systems, GPU orchestration, schedulers, and high-throughput machine learning pipelines.
• Applied Science: Machine learning applied to computational biology, materials science, chemistry, physics, or robotics.
• Full-Stack Autonomy: You combine scientific intuition with production engineering, shipping fast, fault-tolerant Python and Rust to test hypotheses at scale.