Senior Machine Learning Engineer

Longshot Systems; Greater London; Hybrid; GBP 90,000 - 140,000; Full time

Benefits offered by this job

Participation in uncapped bonus

10% matched pension

Private healthcare

Long term illness insurance

Gym membership

Job summary

Longshot Systems is hiring Machine Learning Engineers to design, build and productionise ML pipelines for sports betting analytics. You will work with modern Python ML libraries, data engineering workflows and scalable architecture to support research and production trading models.

The role is hybrid, based in London with Thursdays in the office. Expect collaboration with quantitative research teams and a delivery-focused culture emphasizing robust tooling and performance.

Qualifications

• A degree in a quantitative, technical subject from a top university.
• Significant software engineering skills and experience on the modern Python ML stack.
• Experience designing and maintaining ML pipelines and data engineering workflows.
• Familiarity with CI/CD, containerisation (Docker, Kubernetes) and automated testing.
• Experience with cloud platforms (AWS, GCP or Azure).
• Comfortable working in a Linux environment.

Responsibilities

• Design, build and productionise ML pipelines and tooling for analytics and trading teams.
• Shape high-level architecture of strategy software for scalability and low latency.
• Write clean, maintainable Python code and robust data processing workflows.
• Collaborate with quantitative researchers to move prototypes to production systems.

Skills

Software engineering

Python ML stack

ML pipelines

Data engineering

CI/CD

Linux

High performance computing

Multi-threading

C/C++

Education

Bachelor's degree in a quantitative field

Tools

Docker

Kubernetes

Dagster

Prefect

Pandas

Polars

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 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. You'd also help shape the high-level architecture of our strategy software so it scales effectively and keeps trading latency low. Our ML stack is Python based and utilises modern ML libraries and tooling including Numpy, Scipy, Pytorch, Polars, Ray, Plotly, Dash etc.

The ideal candidate will have a strong software engineering background with a track record of building and maintaining production-grade ML pipelines. We are looking for engineers who are comfortable designing robust data engineering workflows, building reliable tooling, and writing clean, maintainable Python code. You should be proficient in modern Python ML and data processing libraries, with a focus on building systems that are scalable and easy to support. Knowledge of common ML algorithms is a plus, but your primary strength should be in software design and productionisation.

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) - learn more about your background + discuss the role
• Technical interview - Python software engineering assessment
• Full assessment day (10:00-5pm) - a one day programming exercise designed to be similar to the real work we do in the team

Requirements

• A degree in a quantitative, technical subject (e.g. Machine Learning, Maths, Physics, Computer Science etc) 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
• 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
• Nice to have:
• 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)
• Experience with C/C++

Benefits

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