Senior Machine Learning Engineer

Longshot Systems Ltd; Greater London; On-site; GBP 45,000 - 65,000; Full time

Benefits offered by this job

Participation in company bonus scheme

10% matched pension contributions

Private healthcare insurance

Long term illness insurance

Gym membership

Job summary

A technology company in the United Kingdom is seeking a Machine Learning Engineer to join their modeling engineering team. The role involves converting trading models into production systems, designing necessary tooling, and supporting strategy research and development. The ideal candidate will have a strong foundation in Python and C++, alongside experience in high-performance computing. Hybrid working is supported, and various perks such as a bonus scheme and healthcare are offered.

Qualifications

• Strong software engineering background.
• Significant experience with the Python ML stack.
• Systematic and analytical approach to problem-solving.

Responsibilities

• Convert prototype trading models into production-ready systems.
• Design and build tooling, frameworks, and data engineering for strategy research.
• Architect high-level design of strategy software to minimize trading latency.

Skills

Python programming

C/C++ proficiency

High performance computing

Multi-threading

Data engineering

Education

Degree in a quantitative, technical subject

Tools

NumPy/SciPy stack

Linux platforms

Data engineering libraries (Dagster, Prefect)

Machine learning libraries (scikit-learn, Pytorch, Tensorflow)

Job description

At Longshot Systems we build advanced platforms for sports betting analytics and trading.

We're hiring Machine Learning Engineers for our modelling engineering team. You'd be working closely with the quantitative research teams to turn prototype trading models into production-ready systems, design and build the tooling, frameworks and data engineering required to support strategy research and development as well as architecting the high-level design of the strategy software to minimise trading latency and scale effectively. Our ML stack is Python based and utilises modern ML libraries and tooling including Polars, Ray, Plotly etc.

The ideal candidate will have a strong software engineering background, with broad experience across a range of topics related to general high performance computing such as multi-threading, networking, profiling and optimisation. Experience working with the NumPy/SciPy stack is essential, as is experience with tools like C++, Numba etc for performance optimisation. Knowledge of common ML algorithms & techniques is a plus, although not essential.

We are a hybrid working company, working Thursdays in our London (Farringdon) office and flexible the rest of the week. Our typical working hours are 10 am to 6 pm UK time, Monday to Friday, but we support flexible working and trust our team to manage their own schedules to meet their goals.

Our interview process is as follows:

• Intro call (30 mins) - your background + interests
• 1st Technical interview (30 mins) - live code review & pair programming
• 2nd Technical interview (60 mins) - deep dive technical questions
• Full assessment day (10:30–5pm) - a one day programming exercise designed to be similar to the real work we do in the team

• A degree in a quantitative, technical subject (e.g. Machine Learning, Maths, Physics) from a top university
• Significant software engineering skills and experience, especially on the modern Python ML stack
• Takes pride in engineering excellence and encourages best practice in others
• A systematic, analytical approach to tackling problems and designing solutions

Experience with:

• Python programming
• Proficient in C/C++ on modern architectures
• Experience with the NumPy/SciPy stack
• Working with Linux platforms with knowledge of various scripting languages
• Strong general high performance computing:
  • Multi threading
  • Profiling Python/C/C++ and performance optimisation
  • Networking

Nice to have:

• Data engineering experience in Python, e.g. with libraries like Dagster, Prefect etc
• Experience optimising dataframe code, e.g. in Pandas or ideally Polars
• Experience of machine learning techniques and related libraries and frameworks e.g. scikit-learn, Pytorch, Tensorflow etc
• Experience in scientific computing with other languages & frameworks

• Participation in the uncapped company bonus scheme, typically 15-25% of salary depending on experience
• 10% matched pension contributions
• Private healthcare insurance
• Long term illness insurance
• Gym membership