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Millennium

Senior Quantitative Engineer, Systematic Cross Commodity

London
Posted 1 day ago
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Senior Quantitative Engineer, Systematic Cross Commodity

Please direct all resume submissions to QuantTalentEUR@mlp.com and reference REQ-30456 in the subject line.

Job Description

We are a small, collaborative systematic trading team based in London looking for a senior engineer to aid in the implementation and continued development of our team's core software and technical infrastructure. The role involves the development and maintenance of sophisticated tools for alpha research along with the production systems used in feature engineering, portfolio construction, and trade execution.

The ideal candidate should be an expert engineer with a deep theoretical foundation, extensive systems design experience, and significant expertise in both high-level and systems programming languages (we primarily use Python and C++) and should be motivated by the idea of playing a pivotal part in a high-impact, technology-driven business. Besides strong technical skills, we value exceptional attention to detail, a strong intuition for the pragmatism-robustness tradeoff and, most importantly, someone who works well in a close-knit, start-up-style team.

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I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?

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Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.

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Graduate Consultant — 2026 Scheme

PwC·London, UK
£35,000/yr

Why you're a good match

Strong

Your economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.

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Why you're a good match

You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.

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Experience fit

Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.

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Location

London

Principal Responsibilities

  • Develop sophisticated research tooling to enable and accelerate alpha discovery.
  • Develop real-time event-driven systems for signal computation, trade-decision-making and execution.
  • Design, implement, and maintain the core systems and services to enable real time data ingestion, retrieval and distributed compute for both research and production.
  • Oversee the ongoing operation of all components within the systems landscape to ensure resilience and detect defects as they arise.

Preferred Technical Skills

  • Exceptional programming skills in both high-level and low-level languages (Python & C++ or similar).
  • Familiarity with modern distributed computing platforms (specifically: docker, kubernetes, ceph, mongodb & kafka).
  • Theoretical proficiency in numerical computing, online algorithms, data structures, networking, databases, and operating systems.
  • Familiarity with typical quantitative research toolchains including Numpy, Polars, Scikitlearn, Pytorch, etc.
  • DevOps: version control, testing frameworks, release processes, build systems.
  • Excellent communication, problem-solving, and analytical skills.

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Preferred Experience

  • Extremely strong computer science or engineering background with 5+ years of experience.
  • Experience designing and implementing:
    • Distributed Systems.
    • Real-time event-driven systems.
    • Large-scale time series data ingress, storage and processing.
  • Experience with the architectural design of large-scale software systems.
  • Experience with systematic futures trading.
  • Exposure to CICD-style implementation/release methodologies with a large complex codebase.
  • Master’s or PhD in Computer Science, Physics, Engineering, Statistics, Applied Mathematics, or related technical field.

Additional Relevant Experience

  • Prior role as a quantitative developer supporting a multi-asset systematic trading business.
  • Experience with a broad spectrum of finance-relevant data sources (e.g. tick data, fundamental data and alternative data).
  • Functional understanding of foundational trading & risk management concepts.

Target Start Date

As soon as possible

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Skills

Python
C++
Distributed Systems
Docker
Kubernetes
Ceph
Mongodb
Kafka
Numerical Computing
Data Structures
Networking
Numpy
Polars
Scikitlearn
Pytorch
DevOps

Location

London, England, United Kingdom

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