AI Scientist

Location: Remote (Monthly on-site visits to headquarters in Colorado are required)

Note: Candidates must be authorized to work in the United States. We do not sponsor H-1B visas or other work authorization.

Role Overview

Our client is a fast-growing fintech company building the future of B2B payments, helping small businesses get paid faster, operate smarter, and stay focused on what matters. AI/ML is a core capability for this organization, not a side project. They use AI to eliminate manual work, automate decisions inside payment flows, and empower small teams to deliver 10X outcomes at 1X cost.

The AI Scientist is a highly influential, hands-on role working directly with the CTO and senior leaders across product, engineering, business, and operations. This person will identify high-leverage opportunities and deliver AI/ML solutions that materially improve outcomes for small businesses, owning AI/ML systems end-to-end from problem framing through production operations.

This is a strategic, hands-on role for a builder who combines a strong research foundation, a track record of shipping ML systems into production, and a modern, pragmatic AI mindset focused on outcomes, leverage, and velocity.

What You'll Do

Customer-Facing AI (Primary)

Build AI/ML solutions embedded directly in B2B payment flows, including:

• 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

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, data quality checks, model monitoring, drift detection, retraining, and reliability SLAs
• Operate ML systems in mission-critical environments: incident response, rapid mitigations, 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) and clearly communicate results and tradeoffs

Strategic Influence

• Help shape the company's AI technical direction and standards as it scales
• Define not just models, but how AI is used responsibly, reliably, and at scale across the organization
• Favor reusable, extensible architectures over one-off models or demos

What 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
• Hands-on experience with modern AI stacks (LLMs, vector databases, orchestration frameworks)
• A mindset focused on leverage, simplicity, and results — not process or legacy approaches
• Bias toward action, experimentation, and measurable outcomes
• MS or PhD in CS, ML, Statistics, Applied Math, or a related field — or equivalent industry experience

Preferred Qualifications

• 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

Ripple Talent is committed to creating a diverse environment and is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, religion, color, sex, age, national origin, ancestry, disability, marital status, veteran status, sexual orientation, or any other basis protected by law.