Senior Machine Learning Engineer

Who are Liberis?

Liberis is on a mission to supercharge the power of small businesses all over the world - delivering the financial products they need to grow through a network of global partners.

Before all else, Liberis is a technology company, connecting finance with small businesses.

We use data to help partners understand their customers’ real time needs and tech to offer tailor-made funding and financial products. Empowering small businesses to grow and keep their independent spirit alive is central to our vision.

Up to now we have funded almost 40,000 small businesses with over $1.5bn - but we believe there is much more to be done.

The role

Joining a multi-skilled team you will be empowered to push the boundaries of the impact AI can have within a scaling, multi-product FinTech organisation. Our tech stack is based in GCP and our Engineering & Product Teams are co-located in our London office - ensuring you will have direct access to decision makers and colleagues to drive projects forward quickly, with collaboration playing a key role. 

Autonomy and independence is in abundance at Liberis, with this role playing a key part in growing our ML operation, influencing how we design our approach, with a focus on innovation, performance and scalability. We want you to be empowered to solve complex data problems and take ownership of state of the art tools to optimise & productionise solutions.

What you'll get to do:

• Design, develop, and deploy end-to-end machine learning systems in Python, ensuring reliability, scalability, and performance.
• Collaborate with data scientists and engineers to integrate machine learning models into production systems, focusing on the quality and maintainability of solutions.
• Work independently to address technical challenges in machine learning pipelines and model deployment.
• Apply MLOps best practices in GCP, including versioning, reproducibility, monitoring, and observability, using tools like Weights & Biases to enhance model tracking and experimentation.
• Collaborate closely with cross-functional teams and communicate technical concepts effectively to both technical and non-technical stakeholders.

Interview process:

1. Screening call with Chess (Internal recruiter)
2. Video interview with the Hiring Manager
3. Tech interview with the ML Team (project discussion)
4. Tech interview with the ML Team (skills discussion)
5. Interview with the Engineering Manager 

What you'll bring:

• Hands-on experience in an ML engineering role, with a track record of developing and deploying machine learning models in production.
• Strong expertise in Python, including data analysis libraries such as Pandas and Numpy, and machine learning frameworks like PyTorch or TensorFlow.
• Proficiency in MLOps tooling, including version control, CI/CD, and model monitoring with tools like Weights & Biases.
• Deep understanding of machine learning concepts, including optimisation, statistics, and algorithm development.
• Solid understanding of classical ML algorithms (e.g., Logistic Regression, Random Forest, XGBoost) and modern deep learning techniques (e.g., BERT, LSTM).

Next Steps

If this opportunity feels like the right fit for your next career move, we’d love to hear from you! Even if you don’t meet every requirement, don’t hesitate to apply or reach out to Chess (Internal Recruiter) at [email protected]

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