AI Scientist

AI Scientist:
Shape the future of payments powered by AI/MLBillGO is building future of B2B payments, helping small businesses get paid faster, operate smarter, and stay focused on what matters, while BillGO accelerates their payments and automate the complexity end-to-end.

AI/ML is a core capability at BillGO, not a side project. We use AI to:

Eliminate manual work for customers and internal teams

Automate decisions and workflows inside payment flows

Empower small teams to deliver 10X outcomes at 1X cost

We are hiring an AI Scientist who can turn this belief into shipped, production-grade systems. This is a highly influential, hands-on role. You will work directly with the CTO and senior leaders across product, engineering, business and operations to identify high leverage opportunities and deliver AI/ML solutions that materially improve outcomes for small businesses.

The Role This is a strategic hands-on AI/ML role for a builder who combines:

A strong research foundationA track record of shipping ML systems into productionA modern, pragmatic AI mindset focused on outcomes, leverage, and velocity

You will own AI/ML systems end-to-end from problem framing through production operations—across both:

Customer-facing AI products for small businesses

Internal AI systems that radically increase BillGO’s operational leverage

What You’ll Do Customer-Facing AI (Primary)
Build AI/ML solutions embedded directly in B2B payment flows, such as:

Intelligent payment acceleration and prioritization

Cash-flow forecasting and predictive insights

Automated reconciliation, exception handling, and workflow orchestration

Decisioning systems that remove work rather than add alerts

Design models that balance accuracy, latency, explainability, and reliability for business-critical systems

Own model behavior in real-world conditions, not just offline metrics

Internal AI Leverage (Equally Important)
Partner with Engineering, Product, Ops, and Finance to:

Automate internal workflows using ML and LLMsReplace manual reviews and heuristics with intelligent systems

Reduce cost-to-serve while increasing throughput and quality

Build AI tools that allow small teams to operate like large ones

Responsibilities

End-to-End Ownership Own the full ML lifecycle: problem definition, data exploration, feature engineering, modeling, evaluation, deployment, monitoring, and iteration

Translate ambiguous business problems into clear ML objectives and success metrics

Production Systems & Operations Build and maintain production-grade ML systems, including:

Batch and real-time pipelines

Feature generation and data quality checks

Model monitoring, drift detection, retraining, and reliability SLAsOperate ML systems in mission-critical environments:

Participate in incident response and rapid mitigations

Design safe rollouts, fallbacks, and guardrails

Own models once deployed, including ongoing performance, reliability, and evolution over time Experiments & Metrics Design and run experiments (offline and online / A-B testing where applicable) and clearly communicate results and tradeoffs

Collaboration & Architecture Collaborate deeply with Product and Engineering to embed AI directly into customer and internal workflows

Favor reusable, extensible architectures over one-off models or demos

Strategic Influence Help shape BillGO’s AI technical direction and standards as the company scales

Help define not just models, but how AI is used responsibly, reliably, and at scale across the company

What You Bring5+ years of proven experience building and shipping ML systems into production with measurable business impact

Strong foundation in machine learning (modeling, training, evaluation, deployment), statistics, and experimentation

Fluency in Python and modern ML tooling (e.g., PyTorch, Tensor Flow, scikit-learn)
Comfortable owning data pipelines and featurization (not dependent on others to make data “model-ready”)Experience working with large, messy, real-world datasets

Ability to clearly explain models, tradeoffs, and outcomes to non-ML stakeholdersA mindset focused on leverage, simplicity, and results not process or legacy approaches

Hands-on experience with modern AI stacks (LLMs, vector…