Lead the development and execution of AI and machine learning strategy for biometric face verification and security.
Compensation
Comp: Negotiable (Base) + Company Performance Bonus (20%) + EMI Share Options + UK iProov Benefits
Responsibilities
Define and steward the AI strategy, roadmap, and vision across core AI research, product, and production domains
Lead teams across research, production/engineering, and MLOps to deliver robust, secure, explainable models for liveness assurance and fraud detection
Oversee model development and deployment pipelines: data ingestion, training, optimization, evaluation, monitoring, and continuous improvement in adversarial biometric threat landscapes
Ensure AI is aligned with product and commercial goals, measuring ROI and outcomes of AI deployments
Drive partnerships and external collaboration with academic, industry, and regulatory stakeholders
Requirements
Master’s or PhD in computer science, machine learning, data science, or a AI related field
Design, build, and optimize AI/ML pipelines for real‑time and batch inference
Collaborate with data engineers and software developers to integrate models into TymeX's banking platform
Research, prototype, and productionize models in areas such as credit scoring, fraud detection, transaction classification, personalization, and conversational AI
Implement robust model evaluation, A/B testing, and drift detection frameworks
Contribute to internal frameworks and libraries to standardize ML development workflows
Build and maintain machine learning systems and models for MoneyLion’s products, leading automation, architecture, and best practices for the Data Science team.
Responsibilities
Frame and contextualize machine learning problems
Research, build and design machine learning systems and models
Assess and analyze data for modelling
Identify, prototype and implement machine learning algorithms
Design, execute and monitor model experiments and metrics
Requirements
Nice to have: MLOps tools such as DVC, MLFlow, Metaflow, Seldon, BentoML
Nice to have: Experience building ML systems and workflows
Build and ship production-ready software using AI as a core part of the engineering lifecycle. Apply AI-native practices to develop and validate solutions, maintain quality and compliance, and continuously improve how software is built and operated.
Responsibilities
Lead the design and implementation of advanced AI and deep learning solutions
Drive the technical strategy and mentor engineering teams
Collaborate with product and data science teams to build scalable, high-performance AI systems
Lead architecture decisions and oversee cloud-based AI deployment on Azure
Requirements
Bachelor's or Master's degree in Computer Science, Engineering, or related field
PhD or advanced research background in AI/ML or related fields
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