Quantitative Developer, Systematic Equities

Millennium Management LLC
London
1 year ago
Applications closed

Related Jobs

View all jobs

Data Scientist (NLP & LLM Specialist)

Senior Data Scientist

Junior / Graduate Data Scientist

Senior Machine Learning Engineer

Data Analyst - Farming Operations

Data Analyst - Farming Operations

Job Description: Quantitative Developer, Systematic Equities

Please send resume submissions to and referenceREQ-19460in the subject line.

Millennium is a top tier global hedge fund with a strong commitment to leveraging market innovations in technology and data to deliver high-quality returns.

A small, collaborative, and entrepreneurial systematic investment team is seeking an experienced developer to join in building critical trading infrastructure. This opportunity provides a dynamic and fast-paced environment with excellent opportunities for career growth.

Location: London

Principal Responsibilities

  1. Partner closely with the Portfolio Manager to develop data engineering and prediction tools primarily for the systematic trading of equities.
  2. Develop software engineering solutions for quantitative research and trading
    • Assist in designing, coding, and maintaining tools for the systematic trading infrastructure of the team.
    • Build and maintain robust data pipelines and databases that ingest and transform large amounts of data.
    • Develop processes that validate the integrity of the data.
  3. Implementation and operation of systems to enable quantitative research (i.e., large scale computation and serialization frameworks)
    • Live operation of such systems, including monitoring and pro-active detection of potential problems and intervention.
  4. Stay current on state-of-the-art technologies and tools including technical libraries, computing environments, and academic research.
  5. Collaborate with the PM and the trading group in a transparent environment, engaging with the whole investment process.

Preferred Technical Skills

  1. Master’s or PhD in Computer Science, Physics, Engineering, Statistics, Applied Mathematics, or related technical field appropriate to a computational background.
  2. Expert in C++.
  3. Advanced programming skills in Python.
  4. Strong Linux-based development.

Preferred Experience

  1. Extremely strong computer science or engineering background with 3+ years of experience.
  2. Approx. 3-4 years of professional experience in a computer science/computational role.
  3. Experience working in a technical environment with DevOps functions (Google Cloud, Airflow, InfluxDB, Grafana).
  4. Design and implementation of front-office systems for quant trading.

Highly Valued Relevant Experience

  1. Knowledge of machine learning and statistical techniques and related libraries.
  2. Experience as a quantitative developer supporting an intraday (or faster) system.
  3. Experience with the development practices of large tech (Google/Meta, etc.) or finance firms.
  4. Experience with financial data.

Target Start Date: As soon as possible

#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.