Head of AI and ML

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About This Role

Xplor provides cloud-based, intuitive software solutions and secure payment services designed to help small and medium-sized businesses manage their daily operations. The company is currently hiring a Head of AI and ML to lead evaluation science, build predictive models, and shape AI-native research and development. This is a hands-on position where at least half of the working hours involve writing code, building models, running experiments, and creating evaluation harnesses. The…

Job Description

Responsibilities

• Establish enterprise evaluation methodology including golden-set design, sampling, labeling, LLM-as-judge validation, and statistical thresholds
• Monitor quality signals such as task success, groundedness, hallucinations, and escalation rates
• Build and deploy predictive and decision models for churn, retention, payment recovery, demand forecasting, and onboarding risk
• Analyze model economics by tracking accuracy, cost, and latency per task to drive down execution expenses
• Prototype techniques like agent memory, planning, multi-agent patterns, and synthetic data against business challenges
• Lead statistical design for benchmarks, indices, and entity-resolution modeling
• Teach evaluation practices, experimentation, and probabilistic thinking through internal training and office hours

Requirements

• Over twelve years of experience in data science, machine learning, or applied research
• Minimum of four years leading teams of scientists or machine learning engineers
• Demonstrated experience training, evaluating, deploying, and maintaining production-grade models beyond consuming APIs
• Recent history of shipping large language models or agentic systems to production along with gating evaluation suites
• Deep expertise in experimental design, causal inference, and statistics
• Proficiency with AI-native tools such as agentic coding assistants, model context protocols, and agent frameworks
• Ability to write production-quality Python and SQL code while reviewing peer submissions
• Experience working with multi-tenant SaaS or marketplace data at scale

Qualifications

• Background in building subscription or SMB software models covering churn, retention, propensity, or pricing
• Experience with payments machine learning focused on recovery, dunning optimization, fraud, or risk scoring
• Familiarity with fine-tuning, model distillation, small-model optimization, and LLM-as-judge design
• Experience utilizing Snowflake, feature stores, Datadog, or OpenTelemetry
• Knowledge of standards and regulations including NIST AI RMF, ISO/IEC 42001, the EU AI Act, and international privacy rules
• History of published research or open-source contributions

Core Skills

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