SentiLink

Artificial Intelligence

• Partner with functional leaders to identify high-leverage processes suitable for AI enablement.
• Map workflows, quantify baseline performance (cost, time, quality), and prioritize opportunities based on ROI and feasibility.
• Develop clear business cases for AI investments, including expected impact and risks.
• Lead end-to-end deployment of AI tools and workflow redesign across pilot functions.
• Redesign processes to embed AI natively rather than layering tools onto existing inefficiencies.
• Coordinate cross-functionally to ensure adoption, training, and operational continuity.
• Ensure compliance with data security, privacy, and risk standards.
• Define standardized metrics to assess AI impact (e.g., cost per unit, throughput, error rates, headcount leverage).
• Establish dashboards and reporting cadences to track performance.
• Deliver sustained productivity improvements in pilot processes, with documented performance over at least six months.
• Present regular updates to executive leadership on outcomes, risks, and expansion opportunities.
• Develop and document a repeatable AI evaluation and implementation framework.
• Create internal playbooks, governance guidelines, and training materials for responsible AI usage.
• Establish an intake process for AI ideas across the company.
• Expand the program beyond initial pilots as results are validated.

AI Productivity Engineer

Data Scientist

• Develop and maintain SentiLink’s fraud detection models through the full model development lifespan: from data acquisition decisions through featurization, focusing labeling resources, model training, experimentation, productionalization, and monitoring.
• Build foundational modeling to drive SentiLink’s expanding suite of Fraud and Financial Risk products.
• Research new types of fraud and develop new SentiLink products around identity verification.
• Achieve success by researching / developing through iteration, integration of new data sources and inventive feature engineering.
• Write production-ready code that can be relied on for real-time decision making by our partners.
• Design, perform, and present analyses that will inform data acquisition, product development, risk operations priorities, marketing, and sales efforts.
• Work with engineering, risk operations, and data acquisitions to access necessary data, maintain data quality, and support data access

Staff Data Scientist, Full Stack

Data Scientist, New Grad

AI Process & Productivity Lead

Frontend Engineer

• Code, test, debug, document, and ship and maintain software applications using established coding standards and methodologies
• Designing and building out API’s that will be consumed by the UI to surface application data.
• Partner with product management to drive agile delivery of both existing and new products based on project requirements, UX design, and industry best practices
• Ensure new software meets quality standards by writing unit and end-to-end automated tests
• Work with product, data, and other stakeholders to analyze usage and product metrics to inform key strategic decisions
• Troubleshoot, debug, resolve product issues as they arise
• Work cross functionally to resolve complex partner problems
• Ensure platform and services meet SLA requirements; on call rotation for production issues, along with the rest of engineering

Senior Software Engineer, Frontend

• Conduct comprehensive data evaluations for prospective partners, demonstrating SentiLink's product value through clear data science metrics.
• Analyze performance data from current partners to ensure optimal product use and provide actionable recommendations.
• Collaborate with cross-functional teams, including Product, Data Science, and Engineering, to enhance product performance and address partner-specific needs.
• Deliver ad hoc analyses and reports that provide deep insights into fraud trends, consumer behavior, and partner-specific challenges.
• Communicate complex data findings effectively through visualizations, reports, and presentations tailored to both technical and non-technical stakeholders

Solutions Analytics Data Scientist

Engineering Manager

• Lead and grow a team of full stack engineers building a customer-facing SaaS product, Intercept, and other applications
• Drive strong execution across sprint planning, prioritization, delivery predictability, and technical quality
• Hire, mentor, and develop engineers while maintaining a high hiring bar
• Partner with senior engineers on scalable, secure, and maintainable system design for application services and user-facing features
• Contribute to architectural decisions and support the long-term evolution of the Intercept platform
• Contribute to code as needed
• Ensure high standards for code quality, testing, reliability, and observability
• Support operational excellence, including incident response and production reliability
• Collaborate closely with Product to translate roadmap priorities into clear engineering plans
• Partner with Data Science and Infrastructure teams to integrate models, services, and data systems into the product experience
• Continuously improve engineering processes (CI/CD, code review, testing practices, developer velocity)
• Foster a culture of ownership, accountability, and continuous learning

Engineering Manager, Product Engineering, Full Stack

Full-stack Engineer

• Code, test, debug, document, and ship and maintain software applications using established coding standards and methodologies
• Designing and building out API’s that will be consumed by the UI to surface application data.
• Partner with product management to drive agile delivery of both existing and new products based on project requirements, UX design, and industry best practices
• Ensure new software meets quality standards by writing unit and end-to-end automated tests
• Work with product, data, and other stakeholders to analyze usage and product metrics to inform key strategic decisions
• Troubleshoot, debug, resolve product issues as they arise
• Work cross functionally to resolve complex partner problems
• Ensure platform and services meet SLA requirements; on call rotation for production issues, along with the rest of engineering

Senior Product Engineer, Full Stack

IT Support

• Provision and manage macOS endpoints (Jamf or equivalent), including configuration profiles, patching, and fleet-wide security controls.
• Own full device lifecycle: procurement, inventory, deployment, recovery, and secure decommissioning.
• Administer identity and access management across SaaS platforms (SAML, OIDC, SCIM), including automated onboarding/offboarding.
• Design and enforce role-based access controls and least-privilege policies.
• Own Zero-Trust Network Access (Cloudflare preferred), including posture checks and secure access policies.
• Troubleshoot cross-layer issues spanning device → identity provider → network → SaaS application.
• Deploy and support lightweight office networking, firewalls, and AV/Zoom infrastructure for distributed and off-site environments.
• Build automations using APIs and scripting to eliminate manual IT workflows.
• Partner with Security and Infrastructure on endpoint hardening, monitoring, and incident response.
• Maintain audit-ready controls and documentation to support SOC 2 / PCI compliance.

Senior IT Specialist

Machine Learning Engineer

• Own SentiLink’s real-time ML model monitoring domain, leading the design, implementation, and ongoing improvement of monitoring systems and workflows.
• Own our ML experimentation, model tracking, and versioning infrastructure, ensuring strong reproducibility and visibility across the model lifecycle.
• Drive improvements to the model development process, reducing inefficiencies, improving code quality, resolving DS tooling gaps, and enabling faster iteration.
• Serve as the primary technical owner of key touchpoints and interfaces between Data Science and Engineering/Infrastructure, defining standards and workflows.
• Support efforts to optimize model behavior in production, including latency, reliability, maintainability, and operational best practices.
• Investigate and diagnose model performance issues on an ad-hoc basis, including partner escalations and analysis of model behavior in real-world scenarios.
• Evaluate, prototype, and recommend new ML infrastructure, tools, and data capabilities, partnering with DS to validate impact and support adoption.

Founding Senior Machine Learning Engineer

Product Marketing

• Design and maintain tools and processes to supercharge our GTM engine
• Connect and build the GTM tech stack into a cohesive, scalable system
• Identify and execute on automation opportunities across the full funnel
• Build tools to uncover and surface opportunities for revenue generation
• Work closely with our revenue organization to translate business needs and pain points into clean, scalable solutions
• Deeply understand our GTM motion to proactively identify process and analytics improvements

Go-To-Market Engineer

Backend Engineer

• You are the owner of one large section of our codebase
• Lead the technical direction provide guidance and set the technical direction for our team, ensuring alignment with project goals and industry best practices
• Design and build a suite of API-based decisioning products
• Code, test, debug, document, and maintain products and platform using established coding standards and methodologies
• Partner with product management to drive agile delivery of both existing and new products based on project requirements.
• Ensure new software meets quality standards by writing unit, dependency, integration and end-to-end automated tests
• Troubleshoot, debug, and resolve product issues as they arise
• Ensure platform and services meet SLA requirements; on call rotation for production issues, along with the rest of engineering