Senior Machine Learning Engineer (Python / C++)

Longshot Systems Ltd; Greater London; Híbrido; GBP 90.000 - 120.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Private healthcare insurance

Long term illness insurance

Gym membership

10% matched pension contributions

Descripción de la vacante

Longshot Systems Ltd is hiring Machine Learning Engineers to design, build and productionise ML pipelines and data workflows. You'll work on a hybrid Python/C++ stack, with an emphasis on low-latency components and scalable strategy software for sports betting analytics.

You will collaborate with quantitative research teams to turn prototypes into production systems, optimise architecture, and support cloud and containerised environments. Hybrid London-based role with flexible scheduling.

Formación

• A degree in a quantitative, technical subject from a top university.
• Strong software engineering background with production-grade ML pipelines.
• Experience building low-latency, high-performance components in a hybrid Python/C++ environment.
• Familiarity with CI/CD, containerisation, and automated testing.
• Experience with cloud platforms (AWS, GCP or Azure).
• Comfortable working in a Linux environment.

Responsabilidades

• Design, build, and productionise ML pipelines and data workflows.
• Develop tooling, visualisation, and frameworks for research and trading platforms.
• Collaborate with quantitative research teams to productionise models.
• Help shape architecture for scalable, low-latency strategy software.

Conocimientos

Python programming

C++ programming

ML pipelines

Data engineering

Linux

Software design

Performance optimization

CI/CD

Educación

Bachelor's degree in a quantitative/technical subject

Herramientas

Docker

Kubernetes

AWS

GCP

Azure

Polars

NumPy

PyTorch

scikit-learn

TensorFlow

Dagster

Prefect

Descripción del empleo

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

• 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)

• Participation in the company bonus scheme.
• 10% matched pension contributions
• Private healthcare insurance
• Long term illness insurance
• Gym membership