ML Research Engineer

• 00About

Learn exact programs, not approximate functions.

We train models that learn the exact discrete program behind a set of examples, until the model itself converges into an exact digital circuit. This is not predictive ML. There is no acceptable error bar. A model that's 99% right is wrong, because the goal is a circuit that computes the function, not one that approximates it. (Mapping that converged circuit onto real FPGA fabric is the compiler team's job; you get it to converge.)

You'll work where deep learning, program synthesis, and logic meet: differentiable relaxations of discrete program search, optimization methods that drive a soft model toward a hard one (hardening, sparsification, generalization), and recurrent or stateful architectures that learn exact update rules and collapse cleanly into discrete logic.

The research question is sharp and wide open: can a network be trained until it doesn't approximate a circuit, but becomes one?

• 01Scope

What you'll do.

• Design sharp experiments and run rigorous ablations.
• Diagnose whether failures come from architecture, objective, optimization, or data generation.
• Build minimal tasks that isolate missing primitives or missing inductive bias.
• Implement new training methods for discrete and near-discrete models.
• Analyze soft-vs-hard mismatches and propose ways to close them.
• Shape architectures that are both trainable and that collapse into exact, discrete logic.
• Maintain a high-quality experimental codebase in PyTorch.
• 02Fit

What you'll bring.

• Strong PyTorch and practical deep learning engineering.
• Strong grasp of optimization, gradient flow, and training instability.
• Strong experimental discipline: clean baselines, controlled ablations, reproducibility, and reading negative results honestly.
• A bias toward small, decisive experiments over big, inconclusive ones.
• Strong coding in Python, and comfort reading lower-level code when needed.
• 03Nice to have

Bonus points.

• Neural program synthesis or algorithmic reasoning.
• Formalizing problems as search over discrete structures.
• A feel for when reaching zero training loss really means the model has collapsed into an exact circuit, not a soft approximation that only looks discrete.
• Designing synthetic tasks and curricula.
• Sequence models, recurrent state, or memory-based models.
• SAT/SMT, combinatorial optimization, or search.
• Logic synthesis, circuits, compilers, or PL.
• Differentiable relaxation methods for structured prediction.
• A screenshot of your Factorio megabase