Quantitative Developer – Compute & ML Platform

Millennium; Dublin; On-site; EUR 120,000 - 180,000; Full time

Job summary

Millennium is seeking an experienced software engineer to extend a distributed dataset API and build large-scale ML pipelines. You will work across research and platform teams to improve compute, storage, and model lifecycle in a production environment.

The role emphasizes strong Python skills, modern CI/CD practices, and collaboration with researchers to shape the platform’s evolution in a finance-focused setting.

Qualifications

• 6+ years of Python and distributed systems experience in a quantitative finance environment.
• Bachelor’s degree in Computer Science, Mathematics, Financial Engineering, Operations Research, or related field.
• Deep knowledge of distributed compute frameworks such as Ray, Dask, Spark, PySpark or Slurm.
• Hands-on ML/DL workflows using PyTorch and XGBoost across training and serving lifecycles.
• Strong Python skills and Git/Unix/Linux/Bash with modern CI/CD workflows.
• Experience with kdb+, PyKX, Polars, Arrow, and cloud tooling is a plus.

Responsibilities

• Extend a distributed dataset API with new operators and optimizations for a smooth user experience.
• Design and implement ML pipelines for large-scale training, tuning, and model serving.
• Improve reliability, observability, and multi-user capabilities of the compute stack.
• Build durable abstractions to keep the platform flexible as compute backends evolve.
• Write, test, document, and maintain code with CI/CD and automation.
• Collaborate with researchers and engineers on platform direction and architecture.

Skills

Python

Distributed systems

Communication skills

Quantitative finance domain knowledge

Education

Bachelor's degree in Computer Science, Mathematics, Financial Engineering, Operations Research, or related field

Tools

Ray

Dask

Spark

PySpark

Slurm

PyTorch

XGBoost

Git

Unix/Linux

Bash

Kubernetes

Polars

Arrow

kdb+

PyKX

Job description

About Millennium

Millennium is a global, diversified alternative investment firm, founded in 1989. Defined by evolution, innovation and focus, Millennium’s mission is to deliver results for our investors.

Our people are empowered with both independence and support: the autonomy to pursue ideas with conviction and the backing of a global network committed to collaboration, disciplined risk management and continuous learning. With opportunities to deepen expertise and accelerate development, talent at Millennium is equipped to adapt, evolve and build lasting impact over time. Discover how transformative growth accelerates impact.

Meet the Team

The Execution Services team supports trading across products and geographies, with a focus on execution quality, trading efficiency, and the tools that help portfolio managers make informed decisions. Within this group, Central Liquidity Strategies develops and operates trading capabilities that support internal liquidity solutions across the platform. The team works across quantitative research, platform engineering, and production systems to build the compute, storage, and model infrastructure that supports research and live trading.

What You'll Do

• Extend the proprietary distributed dataset API, a Polars-style, lazily evaluated, DAG-planned DataFrame surface, with new operators, optimizations, and a strong new-user experience
• Design and implement machine learning pipelines spanning large-scale training and tuning, model serving, production validation, and the full retraining lifecycle for a growing model fleet
• Improve the reliability, observability, and multi-user capabilities of the compute infrastructure, including resource management, workload isolation, and clean backend interfaces
• Build durable abstractions that allow the platform to remain flexible as distributed compute backends evolve
• Write, support, maintain, and test code using strong engineering practices, including unit testing, documentation, automation, and CI/CD workflows
• Partner with team members on the direction, design, and architecture of the platform
• Collaborate closely with researchers and platform engineers to align compute and storage capabilities across the environment

What You Bring

• 6+ years of Python and distributed systems experience in a quantitative finance environment, with a strong track record of building platform abstractions used by others in production
• Bachelor’s degree in Computer Science, Mathematics, Financial Engineering, Operations Research, or a related field
• Deep working knowledge of one or more distributed compute frameworks such as Ray, Dask, Spark, PySpark, or Slurm, with a clear understanding of their tradeoffs
• Hands-on experience implementing machine learning and deep learning workflows using tools such as PyTorch and XGBoost, with comfort across the full training-to-serving lifecycle
• Strong Python skills, and optionally kdb+, for platform and research-focused development
• Strong working knowledge of Git, Unix/Linux, Bash, and modern CI/CD workflows
• Strong communication skills and the ability to work effectively in a collaborative team environment
• Experience with kdb+, PyKX, Polars, Arrow, columnar or lazy query engines, cash equities, live analytics, and/or cloud tooling including Kubernetes is a plus