npv labs

Principal Machine Learning Engineer

Applied Machine LearningConfirmed live in the last 24 hours

Full-time

About the Job

Requirements

Exceptional depth in machine learning and substantial experience building and operating production machine-learning systems, including traditional machine learning (ranking, classification, tree-based models, and calibration) and modern deep-learning and large language model systems.
Strong Python and SQL skills.
Experience designing scalable training, inference, and model-monitoring infrastructure in a cloud environment.
Comfort using modern artificial-intelligence tools to accelerate research, development, and experimentation.
Conversational English.

Responsibilities

Set the technical direction for machine learning across Split Pay and own the full lifecycle of machine-learning systems, from problem formulation and experimentation through deployment and monitoring.
Build and improve models across underwriting, credit risk, fraud detection, payment routing, collections, and marketing optimization, combining traditional statistical and machine-learning techniques with foundation models and large-language-model approaches.
Work directly on the underwriting stack, where classical machine-learning systems and the company's foundation-model-based systems operate together, and mentor engineers.

Desired Qualifications

Experience in credit underwriting, lending, payments, fraud, or financial services is a major plus.

We're looking for a Principal Machine Learning Engineer for Split Pay, a freshly funded Series B fintech providing split rental and mortgage payments for the US market. Backed by leading VC funds, with a founding team boasting experience at Cash App, Dropbox, Square, Amazon and Apple, Splitpay is going through extensive growth and wants to dramatically raise the level of machine learning across the company, with an unusually broad mandate spanning underwriting, fraud prevention, payment optimisation and marketing efficiency.

You will

Set the technical direction for machine learning across Split Pay and own the full lifecycle of ML systems, from problem formulation and experimentation to deployment and monitoring.
Build and improve models across underwriting, credit risk, fraud detection, payment routing, collections and marketing optimisation, combining traditional statistical/ML techniques with foundation models and LLM-based approaches.
Work directly on the underwriting stack, where classical ML and the company's own foundation-model-based systems operate together, and mentor engineers along the way.

Requirements

Exceptional depth in machine learning, with substantial experience building and operating ML systems in production, including both traditional ML (ranking, classification, tree-based models, calibration) and modern deep learning/LLM systems.
Strong Python and SQL skills, and experience designing scalable training, inference and model-monitoring infrastructure in a cloud environment.
AI-native working habits – comfort using modern AI tools to accelerate research, development and experimentation.
Conversational English.
Experience in credit underwriting, lending, payments, fraud or financial services is a major plus.
London, UK or Lisbon, Portugal (on-site).

We offer

Up to €220k/year salary + equity; higher figures can be negotiated.
Official employment, visa and relocation help, supplemental medical coverage, wellness benefits, corp. laptop, etc.

About the Company

npv labs

Company size: Not listed
Company stage: Not listed
Total funding: Not listed
Headquarters: Not listed
Founded: Not listed

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