Machine Learning Engineer

Poesis; San Francisco (CA); Hybrid; USD 190,000 - 230,000; Full time

Benefits offered by this job

High quality dental, vision, and health care

Relocation support available

Hybrid work model

Job summary

A pioneering hedge fund in San Francisco is seeking a Founding ML Engineer to architect and build machine learning systems for investment decisions. This hands-on role requires 5–10+ years of experience and proficiency in Python and ML frameworks like PyTorch and TensorFlow. The position offers a hybrid work style and opportunities to work closely with executives. The candidate will be integral in developing reproducible ML pipelines, implementing evaluation frameworks, and establishing robust MLOps practices.

Qualifications

• 5–10+ years of experience as an ML Engineer or similar role.
• Proven track record deploying production ML systems.
• Experience with financial data APIs and real-time data handling.
• Experience with large-scale data pipelines and MLOps.
• Ability to work with executives and own product outcomes.
• Willingness to work in-person in Bay Area; relocation supported.

Responsibilities

• Architect, build, and maintain ML infrastructure for the investment platform.
• Develop reproducible pipelines for data ingestion and model training.
• Implement backtesting and evaluation frameworks with performance metrics.
• Deliver regular reports on model accuracy, features importance, and portfolio impact.
• Collaborate with Chief Scientist to refine hypotheses and production readiness.
• Maintain code quality: versioning, testing, and documentation.
• Build backtesting and validation tools with walk-forward evaluation and risk controls.
• Integrate with financial data providers (Bloomberg, FactSet, Refinitiv, CapIQ).
• Establish MLOps: model versioning, CI/CD, monitoring, docs.
• Define and iterate on demo workflows connecting outputs to decisions.

Skills

Python

ML frameworks (PyTorch, TensorFlow)

Data ingestion

Model training

Version control

Data APIs

Production ML

Leadership

Tools

Bloomberg API

FactSet API

Refinitiv API

Airflow

Docker

Kubernetes

Git

Job description

About Poesis
Poesis is the AI-native investment manager pioneering a new foundation model for investing in U.S. equities. We're building modular AI systems to predict market movements and outperform legacy managers. This is frontier research with immediate real-world validation. Your work will directly shape investment decisions and portfolio performance.

Location & Workstyle
San Francisco Bay Area (near Stanford). Hybrid: several days on-site per week.

Relocation available.

About the role

Poesis is building an AI-driven hedge fund focused on reshaping how trading decisions are made. We’re hiring our Founding ML Engineer, the first full-time machine learning hire who will turn research and data into production models.

You’ll build the first ML pipelines end-to-end — from ingesting and cleaning data, to model training, validation, and signal generation. This is a deeply hands‑on, execution‑oriented role for someone who can write code, design experiments, and deliver validated results quickly.

You’ll work directly with the CEO, CFO, and Chief Scientist, owning both implementation and iteration. Over time, you’ll help scale the system into a full production platform and define best practices for future hires.

Responsibilities

• Architect, build, and maintain the core ML infrastructure for Poesis’ investment platform.

• Develop reproducible pipelines for data ingestion, feature generation, and model training.

• Implement backtesting and evaluation frameworks with clear performance metrics.

• Deliver regular, documented reports on model accuracy, feature importance, and portfolio‑level impact.

• Collaborate closely with the Chief Scientist to refine model hypotheses and production readiness.

• Maintain code quality: version control, testing, reproducibility, and documentation.

• Build robust backtesting frameworks and model validation tools with walk‑forward evaluation and risk controls.

• Integrate with professional financial data providers (Bloomberg, FactSet, Refinitiv, CapIQ).

• Establish foundational MLOps practices: model versioning, CI/CD, monitoring, and documentation.

• Define and iterate on “demo‑able” workflows that connect model outputs to investment decision‑makers.

Required Competencies

• 5–10+ years of experience as an ML Engineer, Quant Engineer, or similar role.

• Proven track record deploying production ML systems (ideally in finance or other high‑stakes domains).

• Deep expertise in Python and ML frameworks (PyTorch, TensorFlow, scikit‑learn, JAX, XGBoost).

• Experience designing large‑scale, reliable data or MLOps systems.

• Strong software engineering fundamentals: testing, versioning, CI/CD, and code review discipline.

• Experience with financial data APIs and real‑time data handling.

• Comfortable working directly with executives and acting as both IC and product owner.

• Willingness to work in‑person in the Bay Area; relocation support available.

Preferred Competencies

• Prior experience at a hedge fund, quant research lab, or fintech startup.

• Familiarity with quantitative finance, portfolio optimization, or risk management.

• Exposure to time‑series modeling, forecasting, or reinforcement learning.

• Understanding of financial market microstructure and execution systems.

• Experience with LLM/RAG workflows for parsing financial documents (filings, transcripts).

• Comfort with multi‑language engineering environments (C++, Rust, Go, etc.).

Profile

• You’re a founder‑type engineer — equally comfortable writing code, setting strategy, and defining requirements.

• You thrive in high‑autonomy, low‑process environments and like being close to decision‑makers.

• You think like both a researcher and a builder, able to turn models into production systems quickly.

• You’re pragmatic: you deliver something useful fast, then refine it as data and users evolve.

• You want to build the technical backbone of a next‑generation hedge fund from day one.

Benefits: High quality dental, vision, and health care

Current legal authorization to work in the US required; visa sponsorship considered later for full‑time employees.