Austin is where Optiver’s ambitions in machine learning, research infrastructure, and big data computing are being realized. Engineers and researchers build and scale systematic trading systems, turning data and models into systems that run in live markets.
Research-driven trading
Our high-frequency trading team
“I was surprised by the opportunities to do a lot of different things. When I first started, I was putting my hand up for all sorts of things. There’s a culture where you can just pick things up and run with them.”
“There’s a real focus on building things with a purpose. Nothing extra, every component has a reason.”
Outdoors, Year-Round
Comedy scene in ATX
Austin
Austin
The competitive environment makes machine learning in trading fundamentally different from other applications of ML. Quantitative Researcher Denis Perevalov shares what makes it interesting.
Large language models and alternative data often dominate conversations about deep learning in trading. At high-frequency horizons, Karun Rao looks instead at the order book, where models learn from noisy, event-based market data whose structure is hard to capture by hand.
A conversation with NVIDIA on how accelerated computing is changing research and engineering in trading.
Optiver’s software engineering function was profiled by The Pragmatic Engineer, a leading technology publication on Substack.
Latency and research iteration pull in different directions. Software engineer Daniel B. explores how three generations of trading systems evolved to balance both.
For the past several years, the AI conversation has largely centered on models: which are the most capable, which will dominate and how quickly they will improve.
