AI Scientist

BillGO, Inc. • Fort Collins, CO

1d

About The Position

AI Scientist: Shape the future of payments powered by AI/ML BillGO 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 foundation A track record of shipping ML systems into production A 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 LLMs Replace 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

Requirements

• 5+ 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, TensorFlow, 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 stakeholders
• A mindset focused on leverage, simplicity, and results not process or legacy approaches
• Hands-on experience with modern AI stacks (LLMs, vector databases, orchestration frameworks)
• Ability to think holistically across data, infrastructure, product, and UX
• Relentless curiosity with a strong builder’s mindset
• Bias toward action, experimentation, and measurable outcomes
• Comfortable making decisions with imperfect information
• Deep motivation to deliver 10X impact with 1X cost
• MS or PhD in CS, ML, Statistics, Applied Math, or a related field or equivalent industry experience
• Research experience that informs better decisions but does not slow shipping

Nice To Haves

• Experience in payments, fintech, B2B platforms, or workflow automation
• Experience with real-time decisioning systems (latency, throughput, reliability constraints)
• Applied experience with foundation models (evaluation, guardrails, fine-tuning, agentic workflows)
• Experience designing decision systems, not just predictive models
• A track record of replacing complex manual processes with simple, automated systems
• Exposure to fraud, risk, or compliance systems.
• AI/ML Certifications, Published papers and contributions to the AI/ML Community.

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 SLAs Operate 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 This Job Offers

• Job Type: Full-time
• Career Level: Mid Level