Senior Software Engineer — San Francisco Bay Area — LinkedIn — 4488859043

Senior Trading Systems Engineer (San Francisco · On-site only)

A small, startup quant trading firm in San Francisco is looking for one senior engineer to own its production trading stack. The firm trains large transformer models on raw market data to generate mid-frequency signals, trades them on its own capital, and already pushes forecasts and orders to partner funds. Research is staffed with strong PhDs, with Engineering being the bottleneck currently.

You will be responsible for the systems that turn model output into real risk and real P&L.

• Own the production trading systems end-to-end: correctness and uptime first, latency second. When something breaks at 3 a.m. or a venue changes its behavior, this person fixes it or prevents it from happening again.

• Build and maintain exchange connectivity and execution across CME, Hyperliquid, and Binance, then systematically expand to additional futures venues. Gateways, order management, risk checks, monitoring, and the feedback loops that keep everything honest.

• Design and harden the signal delivery layer (the firm already streams forecasts over gRPC with protobuf schemas) so partners can trust what they receive and the systems can scale the number of venues and strategies without becoming fragile.

• Set the engineering bar. The firm uses AI coding tools heavily. This person decides what “precise and trustworthy” means for a trading system and ensures the output of those tools meets that standard—without reading every line themselves.

• Work directly with researchers who are already generating live alpha. The job is to make those models production-grade and to keep the infrastructure from becoming the constraint as capital and venue count grow.

Who is this for?

You should have already built production trading infrastructure at a serious trading firm before, covering exchange connectivity, order gateways, execution, risk, monitoring. Ideally, you'll have the taste to know the difference between a system that looks good in a backtest and one that stays correct under real market pressure. You'll be using Rust in production, however if you've only built using C++ before, they'd still like to speak with you.

Why this seat is attractive?

• Research is already overstaffed, Engineering is the constraint. The person in this role defines how the trading system is built and how it scales.

• The firm is live on major venues with its own capital and already delivering signals externally. This is not a research project hoping to trade someday.

• The edge thesis is clear and capital-intensive: better models on raw data, better compute, better predictions. Infrastructure quality is about to become a direct driver of P&L as larger prop partners come online.

• Small team, high ownership, founders with serious quant and physics backgrounds. No layers between the engineer and the decisions that matter.

• Equity is the real upside. Cash is capped around the level of a strong senior hire at a smaller firm; the pitch is the ownership, not the base.