Job Title: Machine Learning Engineer – Finance
Location: London (2 days on-site)
Salary: £60,000 - £80,000 + benefits
About Us
Our client is a fast-growing FinTech startup redefining how mid-sized enterprises manage liquidity, credit, and cross-border finance. Since 2020, they’ve grown to a 60-person team across the UK, closed our Series B, and are backed by top-tier investors including Notion and Accel.
At the core of our platform is intelligent decision-making at scale… and that’s where you come in. We’re now looking for a Machine Learning Engineer who’s ready to take ownership of production-level models that directly impact risk, underwriting, and transaction workflows.
What You’ll Do:
- Design, build, and deploy ML models for real-time credit risk scoring, fraud detection, and dynamic pricing
- Architect and implement end-to-end ML pipelines (from data ingestion and feature engineering to monitoring and retraining)
- Collaborate with product, engineering, and data teams to identify use cases, develop models, and integrate into our core platform
- Experiment with and apply state-of-the-art techniques in NLP, time series, and anomaly detection
- Own model evaluation, explainability, and monitoring frameworks in production
- Stay up to date with developments in the ML/AI ecosystem and bring fresh ideas to the table
What we’re looking for:
- Strong Python skills and experience in ML libraries like scikit-learn, PyTorch, TensorFlow, or XGBoost
- Hands-on experience building and deploying ML models into production environments
- Familiarity with ML Ops workflows (e.g., MLflow, Airflow, Weights & Biases, or Kubeflow)
- Experience working with structured data (credit, payments, customer behaviour) and applying feature engineering at scale
- Understanding of model performance metrics, calibration, A/B testing, and monitoring in production systems
- Experience with cloud platforms (GCP, AWS or Azure), especially managed ML services like SageMaker or Vertex AI
- Proficiency in SQL and working knowledge of distributed computing tools like Spark or Dask
Nice to Have:
- Experience with natural language processing (NLP) e.g., using LLMs, transformers, text classification
- Familiarity with Graph ML (e.g., for customer network analysis or fraud detection)
- Exposure to finance, credit risk modelling, or regulated environments
- Strong software engineering fundamentals, version control, CI/CD, testing
- Previous startup experience or entrepreneurial mindset
What We Offer:
- A chance to work on real-world ML problems that power decisions across millions in daily transactions
- Competitive salary and meaningful equity
- 25 days holiday + bank holidays
- Private healthcare & life insurance
- Generous learning budget + conference support
- An open, inclusive culture where experimentation is encouraged and your voice will be heard
