Senior Machine Learning Engineer
At a glance
Senior Machine Learning Engineering role at Longshot Systems Ltd. London · full-time · no salary stated · we estimate £125,500–£152,500.
Longshot Systems Ltd has not published a salary for this role. The figure below is our estimate, not theirs.
aimlroles summary, based on the employer's posting.
What you'll do
- Create ML pipelines, engineering tools, visualisations and data workflows that support strategy research and model development
- Work with quantitative researchers to move experimental trading models into dependable production systems
- Guide the architecture of strategy software so it can scale while keeping trading delays low
- Build and maintain Python systems using the ML and data-processing libraries named in the role
What you bring
- A degree in a quantitative or technical discipline, such as computer science, mathematics or machine learning
- Strong software engineering experience, particularly with Python and modern machine-learning tools
- A track record building and supporting ML pipelines and data workflows
- Experience with CI/CD, containers, automated testing, cloud platforms and Linux
Who this fits
This senior role suits an engineer focused on software design and bringing ML systems into production. You’ll work in a hybrid setup, with Thursdays at the Farringdon office and flexibility on other days. Typical hours are ten to six UK time, Monday to Friday, with flexibility to manage your schedule.
From the employer
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