Machine Learning Engineer
Role details
Job location
Tech stack
Job description
GoCardless relies on machine learning to power features like payment intelligence and fraud detection. We are expanding our ML capabilities to run models at scale - serving thousands of merchants and processing millions of payments monthly and also serving our internal data products.
As a Machine Learning Engineer, you'll lead end-to-end model work and partner closely with Data Scientists, Data Engineers and other stakeholders.
What excites you
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Architect & Design ML Solutions
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Own architecture for complex ML systems, selecting algorithms, frameworks and infrastructure to solve business problems at scale.
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Translate high-level product requirements into clear modeling objectives, aligning with GoCardless's SLAs and business goals.
End-to-End Model Development
- Partner with our Data Engineering team to ensure reliable data ingestion and preprocessing
- Collaborate with Data Scientists to design features
- Build and maintain training workflows to train, tune and evaluate models.
- Establish reproducible experiment tracking (e.g., Vertex Experiments) for metrics, hyperparameters and model artefacts.
Production Deployment & Monitoring
- Build and maintain scalable, automated pipelines for CI/CD of ML models.
- Deploy models as containerized services (Docker/Kubernetes, Vertex Endpoints)
- Define monitoring and alerting for model performance (drift detection, data quality checks) and collaborate with Data Scientists on model health and iteration.
Cross-Functional Collaboration
- Engage with Product, Software Engineering and Design teams to inform roadmaps, surface technical feasibility, and prioritize ML initiatives
- Liaise with Data Engineers to ensure data schemas, pipelines and validation processes meet ML workflow requirements.
Leadership & Mentorship
- Set ML standards and best practices at GoCardless: establish coding guidelines, conduct design reviews, and evangelize solid ML principles (version control, testing, reproducibility).
- Mentor and coach mid-level and junior ML Engineers and Data Scientists, fostering a culture of continuous learning and high code quality.
Innovation & Research
- Stay current on advances in machine learning and deep learning.
- Evaluate new tools and libraries across cloud platforms, We're an organisation defined by our values; We start with why before we begin any project, to ensure it's aligned with our mission. We make it happen, working with urgency and taking personal accountability for getting things done. We act with integrity, always. We care deeply about what we do and we know it's essential that we be humble whilst we do it. Our Values form part of the GoCardless DNA, and are used to not only help us nurture and develop our culture, but to deliver impactful work that will help us to achieve our vision. Diversity & Inclusion
We're building the payment network of the future, and to achieve our goal, we need a diverse team with a range of perspectives and experiences. As of July 2024, here's where we stand:
- 45% identify as women
- 23% identify as Black, Asian, Mixed, or Other
- 10% identify as LGBTQIA+
- 9% identify as neurodiverse
- 2% identify as disabled
If you want to learn more, you can read about our Employee Resource Groups and objectives here as well as our latest D&I Report Sustainability at GoCardless
Requirements
- Bachelor's or Master's in Computer Science, Machine Learning, Statistics, Mathematics or equivalent.
- 3+ years of hands-on experience designing, training and deploying production ML models - ideally on GCP Vertex AI or a similar managed platform.
- Track record of shipping and running at least two end-to-end ML projects in production.
Technical Skills
- Proficient in Python and SQL; familiarity with additional languages is a plus.
- Deep understanding of supervised and unsupervised learning methods and when to apply them.
- Extensive experience with ML frameworks (TensorFlow, PyTorch, scikit-learn), data processing tools and experiment-tracking platforms (Vertex Experiments, MLflow, Weights & Biases).
- Familiarity with feature stores and metadata management.
- Nice to have - experience with off the shelf ML/AI in SaaS tooling.
ML Engineering & Collaboration Skills
- Solid grasp of MLOps principles: CI/CD for ML, version control, reproducibility, monitoring and automated retraining.
- Excellent problem-solving ability with a data-driven, metrics-focused mindset.
- Strong communication skills: able to explain complex ML concepts to both technical and non-technical stakeholders.
- Collaborative attitude: you thrive working with Data Scientists, Data Engineers and other stakeholders to deliver production-ready models.
Benefits & conditions
Base salary range: £82,320 - £123,480
Base salary ranges are based on role, job level, location, and market data. Please note that whilst we strive to offer competitive compensation, our approach is to pay between the minimum and the mid-point (£82,320 - £102,900) of the pay range until performance can be assessed in role. Offers will take into account level of experience, interview assessment, budgets and parity between you and fellow employees at GoCardless doing similar work. The Good Stuff!
- Wellbeing: Dedicated support and medical cover to keep you healthy.
- Work Away Scheme: Work from anywhere for up to 90 days in any 12-month period.
- Hybrid Working: Our hybrid model offers flexibility, with in-office days determined by your team.
- Equity: All permanently employed GeeCees get equity to share in our success.
- Parental leave: Tailored leave to support your life's great adventure.
- Time Off: Generous holidays, 3 volunteer days, and 4 wellness days annually.
