About the role
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.
Responsibilities
•Designing, building and productionising ML pipelines, tooling, visualisation, frameworks and data engineering workflows to support strategy research, analysis and development
•Working closely with 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
Requirements
•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
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)
•Familiarity with Python data science tools and frameworks (e.g. NumPy, PyTorch, Polars)
Conditions
•Hybrid working company: working Thursdays in the London (Farringdon) office, flexible the rest of the week
•Typical working hours are 10 am to 6 pm UK time, Monday to Friday, but flexible working is supported and the team manages its own schedules to meet goals
•Participation in the company bonus scheme
•10% matched pension contributions
•Private healthcare insurance
•Long term illness insurance
•Gym membership
