Location
London (Hybrid working with Thursdays in the Farringdon office and flexible remote work the rest of the week)

Hours
Typical working hours are 10 am to 6 pm UK time, Monday to Friday, with flexible working arrangements supported

Salary
Competitive salary offered

About the Role
At Longshot Systems Ltd, we build advanced platforms for sports betting analytics and trading. As a Senior Machine Learning Engineer, you will design, build, and productionise ML pipelines, tooling, visualisation, frameworks, and data engineering workflows to support strategy research, analysis, and development. You will work closely with quantitative research teams to transform prototype trading models into production-ready systems. You will also help shape the high-level architecture of our strategy software to ensure it scales effectively and maintains low trading latency. Our ML stack is Python-based, utilising modern ML libraries and tooling including Numpy, Scipy, Pytorch, Polars, Ray, Plotly, and Dash. This role requires a strong software engineering background with a proven track record of building and maintaining production-grade ML pipelines. You will be responsible for designing robust data engineering workflows, building reliable tooling, and writing clean, maintainable Python code. Knowledge of common ML algorithms is beneficial, but the primary focus is on software design and productionisation. Our interview process includes an introductory call, a technical Python software engineering assessment, and a full assessment day involving a programming exercise reflective of real team work.

Experience
- Degree in a quantitative or technical subject (e.g. Machine Learning, Maths, Physics, Computer Science) from a top university
- Significant software engineering skills and experience, especially with the modern Python ML stack
- Strong experience designing and maintaining ML pipelines and data engineering workflows
- Familiarity with modern engineering practices such as CI/CD, containerisation (Docker, Kubernetes), and automated testing
- Experience with cloud platforms such as AWS, GCP, or Azure
- Comfortable working in a Linux environment

About you
- Takes pride in engineering excellence and encourages best practices in others
- Strong software design and productionisation skills
- Collaborative and able to work closely with quantitative research teams

Qualifications
While not mandatory, the following are advantageous:
- Advanced data engineering experience in Python with libraries like Dagster or Prefect
- Experience optimising dataframe code, ideally with Polars or Pandas
- Familiarity with machine learning techniques and libraries such as scikit-learn, Pytorch, Tensorflow
- Experience deploying and serving ML models in production, including model monitoring and real-time inference
- Experience in scientific computing with other languages and frameworks
- Strong general high performance computing skills including multi-threading, networking, profiling, and optimisation
- Experience with C/C++