Head of AI and ML

Xplor; Oregon; On-site; USD 180,000 - 300,000; Full time

Job summary

Xplor in Oregon seeks a Head of AI/ML R&D to determine, with evidence, how AI systems perform, for whom, and at what cost. You come from data science and ML, building today with AI-native tools and agentic development.

The role is hands-on (≥50% modeling, evals and experiments) and leads a small team, setting methods, reviewing work, and ensuring models are measurable, affordable and trustworthy.

Qualifications

Responsibilities

• Evaluation science for every agent, set enterprise eval methodology, manage quality signals in telemetry contract, catch regressions before customers.
• Predictive and decision models behind the shared agents, build churn/retention uplift, payment-recovery propensity, demand forecasting, later fraud risk, select appropriate tool per task.
• Model economics, justify routing accuracy and cost per task, drive cost per successful task down through optimization.

Job description

At Xplor, we believe that helping people make the most of each day is the most rewarding way to spend ours.

We give small and medium-sized businesses cloud-based, intuitive technology solutions that enable them to manage all the hassles of running and growing a business, so business owners can get back to doing what they love. With Xplor Pay, we help businesses get paid quickly and securely – without hidden fees. We built the tech ourselves, and our platform delivers secure, transparent, fast, and accurate payments.

We are unified by our purpose of helping people to succeed. So, when you become part of our team, you also become part of the personal connection that strengthens the relationship people have with Xplor products.

Position overview

The Head of AI/ML R&D decides, with evidence, whether an AI system works, how well, for whom, and at what cost. They come from data science and machine learning, and they build today with AI-native tools and agentic development.

The role owns the science of evaluation across every agent, the predictive and decision models behind the shared agents (churn, demand, payment recovery, fraud), and the experiment design that lets Finance count AI value once, against a holdout.

The role is hands‑on. At least 50% of time goes to building models, evals and experiments; the rest to setting methods, reviewing, and growing a small team. This role makes what engineers ship measurable, cheaper and trustworthy, and supplies the models where an LLM alone is the wrong tool.

Key responsibilities

1. Evaluation science for every agent
   • o Set the enterprise eval methodology: golden-set design, sampling, labelling and inter-rater agreement, LLM-as-judge validation, slice analysis and statistical thresholds.
   • o Own the quality signals in the minimum telemetry contract: task success, groundedness, hallucination and escalation rates.
   • o Re-run evals per BU and tenant on every agent-core upgrade; catch regressions before customers do.
2. Predictive and decision models behind the shared agents
   • o Build and run the models the horizontal pods need: churn and retention uplift, payment-recovery propensity and timing, demand and capacity forecasting, later fraud and onboarding risk.
   • o Choose the right tool per task: classical ML, a small or fine‑tuned model, retrieval, or a frontier LLM.
   • o Ship models to production on the golden path, with monitoring for drift and feedback loops.
3. Model economics
   • o Build the evidence behind model routing: accuracy, cost and latency per task, so routing is a measured decision.
   • o Drive cost per successful task down through