Senior Machine Learning Engineer (Python / C++)
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 production ML pipelines, tooling and data workflows for strategy research, analysis and development
- Collaborate with quantitative researchers to move trading-model prototypes into dependable production systems
- Guide strategy software architecture so it can scale while keeping trading delays low
- Build reliable, maintainable Python and C++ components for performance-critical systems
What you bring
- A degree from a top university in a technical or quantitative subject, such as computer science or mathematics
- Strong Python software engineering skills and substantial experience with modern C++, including C++23
- A track record of building and operating ML pipelines alongside data engineering workflows
- Experience improving and connecting low-latency components across Python and C++ systems
Who this fits
This suits a senior engineer focused on software design, performance tuning and putting ML systems into production. The role is hybrid, with Thursdays at the Farringdon office and usual working hours of ten to six UK time, Monday to Friday; schedules are flexible.
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. We operate a hybrid Python/C++ engineering stack. A large portion of our stack is Python-based (utilising libraries like NumPy, SciPy, PyTorch, Polars, Ray, Plotly, and Dash), but an increasing amount of our most performance-critical systems are written in modern C++ (C++23). We are actively looking to expand our team's C++ expertise to drive these low-latency components forward.
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 alongside high-performance C++ components. You should be proficient in modern Python ML libraries while bringing solid C++ expertise to optimize our performance-critical architecture. Knowledge of common ML algorithms is a plus, but your primary strength should be in software design, performance optimization, 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 & C++ 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
- 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)
Benefits
- Participation in the company bonus scheme.
- 10% matched pension contributions
- Private healthcare insurance
- Long term illness insurance
- Gym membership