TA Quant

We're looking for a Quant Developer to sit at the intersection of software engineering and quantitative finance, bridging the gap between research and production. You'll be building the infrastructure that our trading strategies actually run on, including backtesting frameworks, simulation environments, execution systems, and the tooling that lets researchers move from idea to live deployment quickly and with confidence.This role demands both depth and range. You need to write clean, performant, production-ready code, and you need to understand the financial logic behind what you're building well enough to catch problems before they hit live markets. You'll work closely with our quant research team to translate complex mathematical models into reliable systems that execute in real time with minimal latency. If you're the kind of person who takes equal pride in elegant code and sound market intuition, this is built for you.
Responsibilities
Design and develop backtesting frameworks, research tooling, and simulation environments for quant strategy development
Implement and maintain trading algorithms and execution systems across live crypto markets
Optimize code for performance and low latency across the full research-to-production pipeline
Collaborate directly with quantitative researchers to productionize strategies and ensure research assumptions hold in production
Build and maintain data pipelines for market data ingestion, processing, and storage
Monitor and improve live system reliability, catching and resolving issues before they affect trading outcomes
Requirements
Bachelor's degree or equivalent in Computer Science, Mathematics, Physics, or a related quantitative field
3+ years of professional software engineering experience with a focus on performance-critical or financial systems
Strong proficiency in Python as a primary language, with working knowledge of C++ or another compiled language preferred
Solid understanding of financial markets, trading concepts, and quantitative methods including time-series analysis and statistical modeling
Experience building or working with backtesting engines, execution frameworks, or real-time data systems
Ability to read and reason about mathematical models and translate them into reliable, testable code

We're looking for a Quant Researcher to join us remotely and take direct ownership of the research lifecycle, from raw data exploration all the way through to live strategy deployment. You'll work closely with our trading and engineering teams to develop, test, and continuously refine the models and algorithms that drive real P&L outcomes across crypto markets. This is a high-ownership role with meaningful exposure to live markets from day one. You won't be running support research in the background, you'll be a core contributor shaping the strategies we actually trade. That means working with large and often unconventional datasets, identifying exploitable statistical patterns in volatile and illiquid market conditions, and translating rigorous mathematical research into production-ready code that performs when it counts.
Responsibilities
Develop, backtest, and continuously improve quantitative trading models and algorithms, translating mathematical insights into production-ready code
Collaborate with trading and development teams to refine existing strategies and build new ones with a direct focus on P&L impact
Work through large and complex datasets to identify patterns, validate hypotheses, and stress-test models against real market conditions
Conceptualize valuation frameworks, improve mathematical models, and maintain strategy performance in live environments
Conduct ongoing statistical research into crypto market microstructure and price dynamics using both conventional and unconventional data sources
 
Requirements
Advanced degree in mathematics, statistics, physics, computer science, or a related quantitative field
Strong proficiency in probability and statistics, including time-series analysis, machine learning, and pattern recognition applied to financial data
Hands-on programming experience in Python, with familiarity in R or MATLAB and ideally some exposure to compiled languages like C++
Prior experience in crypto markets, digital asset trading, or algorithmic finance
Proven ability to work in a data-driven research environment with minimal direction
Rigorous, evidence-based approach to problem-solving with the ability to move quickly from hypothesis to testable implementation