Job Details

Job Description

This role is based in San Francisco and requires in‑office presence 5 days a week.

Responsibilities

• 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 pipeline—from 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 researchers, identifying gaps, solving integration issues, and suggesting improvements.

Qualifications

• 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 environments—financial, 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.

Why This Is A Great Opportunity

You are core to the investment engine; 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.

Clean sheet environment with real ownership: Freederie Grove was built from scratch starting in 2023. Systems, tooling, and workflows are still being designed and improved. Real influence, not incremental tweaks on legacy infrastructure.

Integration beats silos: Fundamental and quantitative professionals operate as one team. Engineers and quant developers are expected to understand the investment context.

Elite leadership with scale and credibility: Founders ran and built at Citadel at the highest level. Institutional rigor without the bureaucracy of a mature multi‑manager.

Real assets, real momentum: Launched with $3.5B and scaled to about $11.6B by Q1 2025 with only 6 clients.

Engineering that matters: 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.

Broad exposure without being spread thin: Market neutral, multi‑strategy equity book across 6 sectors; focused coverage and fewer pods.

High bar, serious peers: Data science, AI, engineering, investing, risk, and trading teams. Expectation that everyone improves each other.

Strong fit for a Python‑first quant developer: If you are a strong Python engineer who understands equities and wants to be closer to the investment process.

Bottom line: Build core investing infrastructure at a scaled but still evolving fund, with elite leadership, real capital.