Machine learning engineer in sports betting

Longshot Systems Ltd; Greater London; Hybrid; GBP 90,000 - 130,000; Full time

Benefits offered by this job

Company bonus scheme

10% matched pension contributions

Private healthcare insurance

Long term illness insurance

Gym membership

Job summary

Longshot Systems Ltd is hiring Machine Learning Engineers to design, build and productionise ML pipelines, tooling, visualisation and data workflows to support strategy research and development. You will collaborate with quantitative researchers to convert prototype models into production-ready systems.

We operate a hybrid model with a London office, typically in Farringdon, and flexible remote days. Strong Python and modern C++ skills, plus experience with ML libraries, are essential for this

Qualifications

• Degree in a quantitative or technical subject from a top university.
• Strong Python and modern C++ (C++23) software engineering.
• Experience building low-latency components in a hybrid Python/C++ environment.
• Experience with ML pipelines and data engineering workflows.

Responsibilities

• Design, build and productionise ML pipelines and data engineering workflows.
• Help shape scalable high-performance architecture with low trading latency.

Skills

Python

C++ (modern)

Low-latency

ML pipelines

Data engineering

CI/CD

Docker

Kubernetes

AWS/GCP/Azure

Linux

Education

Bachelor's degree in a quantitative subject

Tools

Dagster

Prefect

Pandas

Polars

scikit-learn

PyTorch

TensorFlow

Job description

Описание

At Longshot Systems we build advanced platforms for sports betting analytics and trading. We're hiring Machine Learning Engineers across our core ML engineering and horse racing teams. You'd be designing, building and productionising ML pipelines, tooling, visualisation, frameworks and data engineering workflows to support strategy research, analysis and development, working closely with our quantitative research teams to turn prototype trading models into production-ready systems.

Задачи

designing, building and productionising ML pipelines, tooling, visualisation, frameworks and data engineering workflows to support strategy research, analysis and development, working closely with our quantitative research teams to turn prototype trading models into production-ready systems. help shape the high-level architecture of our strategy software so it scales effectively and keeps trading latency low.

Требования

• A degree in a quantitative, technical subject (e.g. Machine Learning, Maths, Physics, Computer Science etc) from a top university
• Strong software engineering background in Python alongside solid expertise in modern C++ (C++23)
• Experience building, optimizing, and integrating low-latency performance-critical components in a hybrid Python/C++ environment
• Strong experience designing and maintaining ML pipelines and data engineering workflows
• Familiarity with modern engineering practices such as CI/CD, containerisation (e.g. Docker, Kubernetes) and automated testing
• Experience with cloud platforms (e.g. AWS, GCP or Azure)
• Comfortable working in a Linux environment

Будет плюсом:

• Advanced 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 deploying and serving ML models in production, including model monitoring and real-time inference
• Experience in scientific computing with other languages & frameworks
• Strong general high performance computing (multi-threading, networking, profiling and optimisation)
• Familiarity with Python data science tools and frameworks (e.g. NumPy, PyTorch, Polars)

Условия

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.

• Participation in the company bonus scheme 10% matched pension contributions
• Private healthcare insurance
• Long term illness insurance
• Gym membership