Founding AI Engineer

About the role: ZeroEntropy is building the next-generation retrieval engine for AI systems. We’re rethinking search from the ground up: faster, more accurate, and built to serve as infrastructure for the next decade of AI.

As a Founding AI Engineer, you’ll work across research and engineering to design, train, and optimize machine learning systems that push the limits of what’s possible in performance-critical environments.

This is a hands-on role for someone who thrives in ambiguity, understands both the math and the machine, and wants to build core technology, not just use it. You’ll join an early, elite team where your ideas will directly shape the product and architecture. This job is for you if:

• You’ve trained and deployed large models in production and debugged the weird edge cases.
• You’ve implemented research papers from scratch and made them faster, cleaner, and more accurate.
• You care as much about the quality of the data pipeline as the model itself.
• You’re comfortable designing experiments, interpreting noisy graphs, and making decisions under uncertainty.
• You love making beautiful, clean, type-safe code, with the goal of pure functional programming using algebraic data types, and can drop down to C++/CUDA when performance demands it.
• You understand distributed systems and what it takes to scale training and inference pipelines in the real world.
• You want to build a system from the ground up with minimal abstraction and maximum control.

• Deep experience with ML frameworks, experiment tracking, and distributed training.
• Strong foundation in math and CS fundamentals: linear algebra, probability, optimization, algorithms, data structures, and time complexity.
• Experience building and scaling robust data and training pipelines.
• Proficient in Python, with bonus points for C++, Rust, CUDA, or other performance-oriented tools.
• You’re comfortable with Linux, containers, and working close to the metal when needed.

Bonus

• Experience with model compression, quantization, or inference optimization.

Background in information retrieval, NLP, or LLM internals. Familiarity with type-safe functional programming languages (e.g. OCaml, Haskell, SML).

About the role Location = Based in San Francisco or willing to move there. Compensation = Very competitive compensation, equity, and benefits.