Machine Learning Engineer

In Technology Group; London; Hybrid; GBP 50,000 - 80,000; Full time

Job summary

Join a fast-growing FinTech startup as a Machine Learning Engineer, taking ownership of innovative ML models that impact financial decisions. Work collaboratively in a dynamic environment focused on liquidity and credit management. You will utilize your advanced ML skills to contribute to real-world applications, while receiving competitive compensation and benefits.

Benefits

Competitive salary and equity

25 days holiday + bank holidays

Generous learning budget + conference support

Open and inclusive culture

Qualifications

• Strong Python skills with experience in ML libraries.
• Hands-on experience in deploying ML models.
• Understanding of cloud platforms and ML Ops workflows.

Responsibilities

• Design and deploy ML models for credit risk and fraud detection.
• Collaborate with teams to identify use cases and integrate models.
• Stay updated with ML/AI ecosystem advancements.

Skills

Python

Machine Learning

Data Engineering

NLP

SQL

Education

Degree in Computer Science or relevant field

Tools

scikit-learn

PyTorch

TensorFlow

XGBoost

Cloud Platforms (GCP, AWS, Azure)

Job description

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This range is provided by In Technology Group. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

Base pay range

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Head of Data, BI, and AI @ In Technology Group.

Job Title: Machine Learning Engineer – Finance

Location: London (2 days on-site)

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
• Generous learning budget + conference support
• An open, inclusive culture where experimentation is encouraged and your voice will be heard

Seniority level

• Seniority level

  Mid-Senior level

Employment type

• Employment type

  Full-time

Job function

• Job function

  Finance, Analyst, and Information Technology

• Industries

  Financial Services and Technology, Information and Media

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