ML Compiler & System

API & authoring experience. The surface that physics and numerical-computing engineers write against, and, as an open-source library, the product’s face to the world. Idiomatic, natural to Python users, never a separate language bolted onto Python. Clean decorators, legible constraints, sharp errors, real escape hatches.

IR, lowering & backends. A stable IR that lowers to multiple backends, with room to grow and the door kept open for differentiable simulation. Today the computation graph is expressed through Quadrants’ own constructs. Recovering it automatically from ordinary Python is hard, and a long-term goal rather than today’s reality.

Optimization. Turn naive code into strong execution plans: vectorization, register and shared-memory usage, barriers and atomics, cooperative threading, dead-buffer and redundant-traffic elimination, dropping needless synchronization. Move low-level optimization off users and into the platform.

Primitives. Cholesky, eigendecomposition, matmul, reductions, solver kernels. Each owned with its correctness, benchmarks, and regression tracking.

Watching Genesis and the compiler together. Hunt for performance wins on both sides, in Genesis and in Quadrants itself. Propose API changes that cut duplication, improve readability, and reduce platform-specific special-casing. Less to maintain, more that simply works.

The project as a product. Benchmark real workloads, track performance over time, catch regressions early. Stay close to users (Genesis first), find friction and missing abstractions, drive adoption, set direction.

You reason about whole systems and have taste for where complexity should live. You measure before you optimize, and you reach for memory traffic and launch overhead before FLOPs. You have opinions about API design. You write tests and benchmarks as you go, not afterward.

The ground that judgment stands on:

GPU. What optimal looks like and why: access patterns, registers, shared memory, barriers, atomics, threading. CPU intuition a plus.

Python & API taste. Idiomatic Python. A feel for what’s natural to Python users. APIs that free users rather than constrain them.

Compilers. The path from authoring to kernels (IR, lowering, optimization), weighed against maintainability.

And how you work: proactive and largely self-directed. You fetch context across the company instead of waiting for it. You’re comfortable talking to the engineers who use what you build, and at ease designing in the open for users you’ll never meet. Ready to grow into leading a small team (a couple of people, near term).

People adopt Quadrants from outside Genesis. Engineers write clearer code with less boilerplate. Kernels keep getting faster. Performance is tracked, not guessed. The architecture gets easier to extend, not harder.

How you work is up to you. What matters is that your ownership grows over time: blind spots shrink, the parts you can vouch for expand, technical debt goes down, the core stays sound.

That takes treating understanding as real work, not overhead. You read and challenge the codebase instead of just stacking features on it. You take nothing you did not write for granted. You watch the competition and what’s coming next, and you bring it in and ship it.

Use AI agents if they help. They are productivity tools, not code owners. You are.