Machine Learning Engineer

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 isintelligent decision-making at scale… and that’s where you come in. We’re now looking for aMachine Learning Engineerwho’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 inNLP, 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 likescikit-learn, PyTorch, TensorFlow, orXGBoost
• Hands-on experience building and deploying ML models into production environments
• Familiarity withML Opsworkflows (e.g., MLflow, Airflow, Weights & Biases, or Kubeflow)
• Experience working withstructured data(credit, payments, customer behaviour) and applyingfeature engineeringat scale
• Understanding ofmodel performance metrics, calibration, A/B testing, and monitoringin production systems
• Experience withcloud platforms(GCP, AWS or Azure), especially managed ML services like SageMaker or Vertex AI
• Proficiency inSQLand working knowledge of distributed computing tools likeSpark or Dask

Nice to Have:

• Experience withnatural language processing (NLP)e.g., using LLMs, transformers, text classification
• Familiarity withGraph ML(e.g., for customer network analysis or fraud detection)
• Exposure tofinance, 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 meaningfulequity
• 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