Machine Learning Engineer

If the idea of building a brand that empowers citizens with their own financial data, that’s fair, ethical, transparent in its approach to democratising affordable credit for all, with a diverse team who understands humans & human data excites you as much as it does us, we'd love to meet.

Who we are:
Good With, an award-winning fintech startup, has secured VC and Innovate UK funding to support safe and fair lending practices for clients ranging from Tier 1 banks to community lenders. We are now preparing for rapid expansion, scaling the delivery of our proprietary financial behavior analytics platform and AI-driven personalised and gamified financial capability-building app to more lenders and borrowers across the UK and internationally.

The role:
(Please don’t be put off applying because you aren’t a perfect match for our job description. If you are excited about the opportunity, think you can achieve the outcomes we are looking for, but aren’t sure if you tick every box, we’d still love to hear from you.)

We’re looking for an experienced Data/MLOps Engineer with a startup mentality, who will work at the heart of a dynamic, multidisciplinary and agile team. As the more senior data engineer on the team, you’ll spend most of your time working across data and software development teams, ensuring the data science pipeline flows seamlessly as part of the product, focusing on quality, automation and security.

Key responsibilities:
Data pipeline design & management: build and maintain robust, scalable data pipelines for ML model training and inference. Ensure data is clean, versioned, and well-documented. Work with batch and real-time (streaming) data sources
Model deployment and product integration: package and deploy ML models into production environments using tools like Docker, and cloud-native services (e.g., Vertex AI, MLflow); design and manage scalable model inference systems (APIs, batch jobs, or streaming) so they integrate well into the core product user journeys.
Model monitoring & maintenance: implement monitoring for model performance (accuracy, drift, latency). Set up alerts and observability tools to track data/model health in production. Automate retraining workflows based on triggers (e.g., data drift, performance drop).

Role Summary:
End-to-End ML workflow automation: data ingestion, preprocessing, model training, validation, deployment, and monitoring; ensure reproducibility and consistency across environments (dev, demo, prod).
Robust Data Engineering: design and build high-quality data pipelines that feed ML models. Manage feature engineering, feature stores, and real-time data transformation.
Governance & Compliance: track and version data, models, and experiments . Ensure auditability, compliance, and reproducibility of ML workflows.
Collaboration across product roles: work closely with: data Scientists to productionise models; Software engineers to integrate product features and manage infrastructure. Product and Analytics teams to understand data and performance needs.

We’d like to hear from you, if you have…
Demonstrable understanding of best practices in software engineering
Proficiency in at least one general purpose programming language (Typescript/Python) with willingness to learn new languages and technologies
Working productive experience with Linux environment and Docker
Experience running production systems on the cloud infrastructure/platforms (AWS/Azure/GCP) - GCP experience is a plus
Passion for MLOps & Machine Learning Infrastructure tooling (e.g. MLFlow) that you’d like to see implemented at Good With
Enjoy participating in the full lifecycle of the software product: from idea and design, via implementation and user interface, to operational considerations
Be able to write clean code, take pride in your work and value simplicity, testing and productivity as part of your daily routine, always putting user experience first
Fintech/Financial Services experience is a bonus

If you have alternative relevant qualifications and applicable technology certifications we encourage you to apply.

Please note:

We are only able to hire

UK-based

candidates.

The Good Stuff
A few things that make Good With an amazing place to work…
30 days holiday, plus your birthday off
‍ Remote-first working environment, with quarter team meets near the beach.

We meet quarterly in the South West, where GWHQ is based - surfing, climbing and water sports optional at these meets.

‍♀️
Flexi-time, you decide with your team, what works best for you all
Commitment to personal development and career growth. Think upskilling, coaching workshops and progression plans
Award-winning recognised innovative Fintech4Good startup, with internationally renowned advisor/mentor support who support our team members
A people focused, inclusive culture

If you’re looking for your next challenge and opportunity to move up in your career, we’d love to hear from you