Machine Learning Engineer

In Technology Group — London, GB
Published on

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.
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