Software Engineer

Software Engineer, Data (Quantitative Investment Sciences) About the Company Our mission is to be the most successful quantitative investment team in the world. We take a scientific and knowledge-centric approach to understanding the markets. We are seeking an exceptionally talented & experienced individual to join our elite investment team.

As part of a revenue-generating team, you'll have direct impact on P&L and work cross-functionally to drive Edge generation. We value intellectual curiosity over hierarchy, the best idea wins, whether it's a new alpha signal or where to grab lunch. About the Role Data fuels our edge, as a Software Engineer you architect greenfield systems and maintain robust infrastructure, with a focus on data automation, accessibility, quality, and scalability.

You will partner with quantitative researchers to facilitate cutting-edge research & trading: Responsibilities Design and implement scalable software systems for data processing and analysis. Create innovative software libraries & enriched datasets that unlock research & LLM capabilities. Build production systems that deploy research projects into our live trading environment.

Rigorously ensure data quality through automated testing and monitoring. Develop data APIs and services that seamlessly integrate research and trading systems. Pioneer reusable software components and frameworks that accelerate quantitative research.

Qualifications 2+ years of software engineering experience, preferably in fast-paced, data-intensive environments. Required Skills Highly proficient with Python and SQL. Excellent communication skills, both written and verbal.

Strong software design principles and experience with clean, maintainable code practices. Intellectual curiosity with the humility to question assumptions and learn from failure. Proactive ownership mindset — you identify opportunities and drive them to completion.

Preferred Skills Distributed systems, microservices, or event-driven architectures. DevOps practices and infrastructure automation (CI/CD, containerization, monitoring). Cluster compute (Spark, Dask, etc.) and orchestration (Airflow, Dagster, Prefect).

Quantitative trading and/or timeseries/market/alternative data. LLMs, machine learning, or NLP data applications. #J-18808-Ljbffr