Quantitative Research & Development Engineer

Algert Global — San Francisco, CA

Build alpha. Drive portfolios. Engineer the platform.

Algert Global is a boutique investment firm applying data science and machine learning to global equity investing. Since 2003, we’ve partnered with some of the world’s most sophisticated institutional investors across market neutral, extension, and long-only strategies.

At Algert Global our product is returns which means your work directly impacts portfolios, not just codebases. You’ll work side-by-side with experienced portfolio managers and investment professionals, learning how ideas translate into capital and performance in real markets.

We’re looking for a high-agency, intellectually curious builder who wants to operate across the full stack of quantitative investing—from alpha research ↔ portfolio decisions ↔ systems and infrastructure.

What You’ll Do

This role spans the full lifecycle of quantitative investing:

Alpha Research

• Develop novel stock selection signals across ~18,000 global equities
• Own the full research pipeline: idea → data sourcing → cleaning → modeling → backtesting
• Explore alternative data, ML techniques, and new research workflows, we’re especially curious to see you build us an agentic research capablility.
• Work closely with PMs to translate ideas into deployable signals

Portfolio Management & Investment Process

• Contribute to portfolio construction and monitoring
• Ensure research signals produce robust portfolio outcomes
• Participate in rebalancing workflows and trade generation
• Help maintain correctness, consistency, and explainability of the process

Research Platforms & Systems

• Build tools spanning:
• Research workflows
• Portfolio analytics and attribution, including materials for client consumption
• Data pipelines and storage systems
• Improve how portfolio managers and researchers interact with data, models, and results

Engineering, Infrastructure & Workflow

• Optimize development workflows (Linux, IDEs, automation)
• Partner with engineers on CI/CD, DevOps, and system architecture
• Drive adoption of AI-assisted development and research tooling
• Help shape infrastructure decisions across compute, storage, and databases

What We’re Looking For

You’re a builder + researcher + operator:

• 4+ years of experience in Quantitative Investing shop (Citadel, Blackrock, pod shops, etc.)
• Strong interest in markets, trading, or investing
• Able to take ownership from idea → production → portfolio impact
• Curious, analytical, and philosophically minded
• Collaborative but highly self-directed
• Pragmatic: knows when to hack vs. when to engineer properly

Technologies

We use a modern, evolving stack. You don’t need everything—but you should recognize a lot of it and be excited to learn the rest.

Research & Data (alpha → portfolio)

• Python (pandas, numpy, scikit-learn, xgboost, statsmodels)
• SQL (MSSQL, Postgres)
• DuckDB, Arrow, Parquet (columnar analytics stack)
• Polars ↔ pandas (dataframe ecosystems)
• Jupyter / notebooks ↔ script-based research workflows
• Time series analysis, cross-sectional modeling
• Feature engineering, signal pipelines, backtesting frameworks

Portfolio & Analytics (research <-> PM)

• Portfolio construction tools (risk models, optimization, constraints)
• Factor models (e.g., Barra-style frameworks)
• Attribution systems, risk decomposition
• Market data systems, pricing pipelines
• Simulation frameworks, scenario analysis

Data Engineering & Platforms (research <-> tech)

• ETL / ELT pipelines, Airflow / orchestration
• Data lakes & table formats (Parquet, Delta)
• Ibis ↔ SQL ↔ Python interoperability layers
• Streaming vs batch processing systems

Engineering & Infrastructure (tech → enabling everything)

• Python packaging, environments (uv, reproducibility tooling)
• Containers (Docker) ↔ orchestration (lightweight or K8s-style)
• CI/CD pipelines (Git-based workflows, automation)
• Infrastructure as Code (Terraform, Ansible)
• AWS (compute, storage, data services)
• APIs, microservices, backend systems

AI-Augmented Development (cuts across everything)

• Claude Code, MCP, agentic workflows
• AI-assisted research, coding, and data exploration
• Building internal tools that leverage LLMs for productivity

Databases & Storage (tech <-> research)

• Microsoft SQL Server, Postgres, use required and management a plus
• Snapshotting, replication, backup strategies
• Object storage, distributed storage systems

Bonus / Nice to Have

• Proxmox, virtualization, cluster management
• Veeam / backup systems
• Pure Storage / enterprise IBM storage
• Linux systems engineering

Location

San Francisco, CA — in-person collaboration required.

U.S. Work Authorization Required

You must be authorized to work in the United States. We are not able to sponsor visas for this role.