Software Engineer, Backend

Why you should join us

• We’re building a research lab with a real-time feedback loop: the financial markets.

• Our edge isn’t secret data or microwaves - it’s the speed at which we iterate and learn.

• We’re starting in options, where complexity (read: opportunity) is high and most firms still rely on human intuition.

• Join a frontier team with over a decade of experience in quant trading. These aren’t logos from internships - we’ve started and led teams at Optiver, DRW, and Argo AI.

• Real ownership, fast iteration, zero red tape. We’re literally putting our $ where our mouth is.

What you’ll do

• Build the core systems for ingesting data, training models, simulating strategies, and running them live - from scratch.

• Design infra that helps researchers move fast: feature stores, experiment tracking, feedback loops, monitoring.

• Work side-by-side with the founders to test predictions in real markets and learn from every outcome - good and bad.

• Make calls on architecture, tools, and priorities. This isn’t a “here’s the roadmap” kind of role. You hold the pencil.

What we’re looking for

• You’ve built real systems: distributed compute, ML pipelines, or infra that powers actual decisions.

• You’re fluent in languages like Python, C++, Rust, and you know your way around cloud, containers, and databases.

• You care about iteration speed, clean abstractions, and systems that can hold up under pressure.

• You think like a builder and have empathy for researchers. Bonus if you’ve worn both hats.

• Zero finance experience required - just curiosity, drive, and the desire to go deep on a hard problem.

You’ll be given the autonomy to do some of the best work of your life.

Note: This is an on-site role based in San Francisco, CA.

Compensation : $100K - $200K • 0.25% - 1.00%

About attimet

We’re building a research lab that puts its ideas to the test in one of the most complex, information-rich environments in the world: the financial markets.

Much of trading still depends on hand-crafted signals and intuition. We’re approaching it differently - from first principles. We design systems that learn, adapt, and improve with data. Our infrastructure is built to accelerate research: fast iteration loops, real-time feedback, and direct connection between ideas and outcomes.

We’re starting with liquid markets like equities and options - high-dimensional, dynamic systems where traditional pipelines break down. Our goal isn’t just to model them better - it’s to build a platform for experimentation, where every result tightens the loop between theory and practice.

If you care about learning systems, clean abstractions, and applying research in the wild - we’re hiring.