AI Scientist

Job DetailsJob Location: Fort Collins, CO 80528Position Type: Full TimeSalary Range: $132,800.00 - $196,500.00 Salary/yearTravel Percentage: NegligibleJob Shift: DayJob Category: EngineeringAI 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    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  QualificationsWhat You Bring  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  Strongly Preferred  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.