ML Engineer / ML Scientist II

About the Company

Harnham is a specialist recruitment firm focused on data, analytics, artificial intelligence, and technology. It is partnering with a global technology company that develops machine learning solutions for e-commerce fraud prevention and transaction intelligence.

The company's platform helps online retailers identify fraudulent transactions in real time, approve legitimate purchases, and reduce financial losses. It supports approximately 1,000 merchants across more than 100 countries and processes billions of transactions annually.

About the Role

The hiring organization is seeking a Senior Machine Learning Engineer to develop and operate the models behind its real-time fraud detection and transaction decisioning platform.

This role combines applied machine learning, statistical analysis, experimentation, and production engineering. You’ll take ownership of complex ML problems throughout the full lifecycle, from initial research and model development to evaluation, deployment, monitoring, and ongoing optimization.

You’ll work within an established machine learning organization, collaborating with Product, Engineering, and Risk teams to build reliable, scalable solutions.

Key Responsibilities

• Design, develop, evaluate, and deploy machine learning models for real-time fraud detection and transaction decisioning.
• Develop experimental approaches to complex business and machine learning problems.
• Build and improve ML pipeline components to support efficient, repeatable experimentation.
• Design rigorous offline evaluation methods to assess model performance before production deployment.
• Evaluate model architectures, features, and statistical approaches to improve predictive performance.
• Contribute to ensemble modeling techniques that combine multiple models into more effective decision systems.
• Build and operate distributed machine learning workloads using Python, SQL, and Apache Spark.
• Manage models throughout their lifecycle, including development, testing, deployment, monitoring, and optimization.
• Partner with Product, Engineering, and Risk teams to translate business requirements into scalable technical solutions.
• Maintain high standards for testing, documentation, reproducibility, and model monitoring.
• Provide technical leadership and contribute to the broader direction of the machine learning organization.

Required Qualifications

• At least 4 years of professional experience after completing an undergraduate degree in a production machine learning environment.
• Demonstrated ability to independently develop machine learning solutions from experimentation through production deployment.
• Strong proficiency in Python and SQL.
• Hands-on experience with Apache Spark and distributed computing environments.
• Strong understanding of machine learning theory, statistics, experimental design, and model evaluation.
• Experience developing machine learning solutions from the ground up, rather than primarily fine-tuning or integrating existing foundation models.
• Experience building offline evaluation frameworks and comparing different modeling approaches.
• Experience deploying, monitoring, and improving machine learning models in production.
• Ability to work independently on technically ambiguous problems while collaborating with engineering and business teams.

Preferred Qualifications

• Master’s degree or PhD in Computer Science, Statistics, Machine Learning, Mathematics, or a related quantitative field.
• Strong academic or research background in machine learning or statistics.
• Experience with ensemble modeling techniques.
• Experience developing ML systems that process high-volume or real-time data.
• Background in fraud detection, payments, e-commerce, transaction risk, or a related field.

Salary & Benefits

The base salary ranges from $140,000 to $210,000 per year, depending on geographic location and experience level. Compensation may reach the upper end of the range for particularly well-matched candidates.

Additional compensation and benefits include:

• Annual performance bonus and stock options.
• 401(k) matching.
• Medical, dental, and vision coverage.
• Unlimited paid time off.
• 12 weeks of paid parental leave.
• Life and disability insurance.
• Mental health and therapy benefits.
• Professional learning and development resources.

Work Arrangement

The machine learning organization includes approximately 20 professionals across three collaborative teams, each with end-to-end ownership of its services and models.

Work Authorization

Applicants must be U.S. citizens or lawful permanent residents (Green Card holders). Employment visa sponsorship is not available now or in the future, including H-1B, F-1 OPT, STEM OPT, CPT, TN, O-1, E-3, and J-1 authorization.