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
Skills Required
- 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
- Strong general high performance computing experience including multi-threading, profiling, and optimization
- Proficient in C/C++ on modern architectures
- Experience with the NumPy/SciPy stack
- Experience working with Linux platforms and various scripting languages
What We Do
Longshot Systems is a small startup producing high throughput, low latency trading software and tools for use in sports betting markets. Our core systems handle thousands of trading signals per second, all of which must be processed and potentially acted upon with minimal latency. We have similar challenges to high frequency trading shops, but in the sports betting world.
