Quantitative Developer

This role is based in San Francisco and requires being in-office 5 days a week., Design and implement robust data pipelines and tools that bring quantitative research into production (e.g., Dagster, Spark, AWS). Partner with research teams to ensure style factor research outputs are scalable, testable, and integrated into broader systems. Own problem-solving tasks such as automating research workflows, enabling scalable data access, or resolving cross-system compatibility issues. Contribute as a generalist across the entire pipelinefrom ingestion and transformation to orchestration and tooling. Maintain a high standard of engineering quality across data handling, software design, and research tooling. Work autonomously while acting as a reliable partner to quantitative researchersidentifying gaps, solving integration issues, and suggesting improvements., This is not a support role. The quant and technical team sits inside the investing system and works directly with PMs, quants, risk, and trading. Your work impacts PnL, decision quality, and speed. You are building the machinery that actually runs money. Clean sheet environment with real ownership Freestone Grove was built from scratch starting in 2023. Systems, tooling, and workflows are still being designed and improved. That means real influence, not incremental tweaks on legacy infrastructure. If you like building durable systems instead of maintaining old ones, this is the right setup. Integration beats silos This is explicitly an anti pod model. Fundamental and quantitative professionals operate as one team. Engineers and quant developers are expected to understand the investment context, not just tickets. You get exposure to how ideas move from research to portfolio to execution, end to end. Elite leadership with scale and credibility The founders ran and built at Citadel at the highest level. They know what works and what breaks at scale. You get that institutional rigor without the bureaucracy of a mature multi manager. Few funds combine this level of experience with a still lean structure. Real assets, real momentum Launched with $3.5B and scaled to about $11.6B by Q1 2025 with only 6 clients. This is not a concept fund or a rebuild story. Capital is stable, growth is real, and the platform is already operating at meaningful scale. Engineering that matters This role is heavy on Python and systems that touch alpha capture, transaction cost analysis, research tooling, and production deployment. You are the technical glue between models and execution. The work is practical, high impact, and directly tied to investment outcomes. Broad exposure without being spread thin The firm runs a market neutral, multi strategy equity book across 6 sectors, but with focused coverage and fewer pods. You see a wide range of problems without the chaos of dozens of disconnected teams competing internally. High bar, serious peers The team spans data science, AI, engineering, investing, risk, and trading. The expectation is that everyone makes everyone else better and faster. If you want to work around people who care about quality, clarity, and constant improvement, this environment delivers that. Strong fit for a Python first quant developer

Strong Python development skills, emphasizing clean, testable, and efficient code. Deep understanding of data manipulation libraries such as Pandas and Polars. Experience working with SQL and non-SQL databases such as Postgres, Redis, or Mongo. Familiarity with distributed computing frameworks such as Apache Spark. Hands-on experience with AWS or similar cloud platforms. Previous experience in quantitative research environmentsfinancial, academic, or ML-driven. Experience supporting production workflows, ideally using modern orchestration tools such as Dagster or Airflow. Ability to think holistically across systems and ensure alignment across the research and production stack. Strong independent problem-solving instincts., If you are a strong Python engineer who understands equities and wants to be closer to the investment process without being a pure researcher or PM, this role is well scoped. It values technical depth, market understanding, and the ability to ship production grade systems that quants actually use.