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The Forward Deployed Engineer will lead partner integrations, contribute code to core products, and enhance partner support through new features and tooling.

Lead end-to-end data science projects to enhance Parafin's underwriting engine by developing predictive models, optimizing pricing, and collaborating with engineering teams.

Manage post-sale onboarding, engagement, retention, and growth for strategic platform partners. Drive account expansion, monitor KPIs, lead business reviews, diagnose issues, and coordinate cross-functional teams to improve product adoption, capital origination, and partner satisfaction.

The Staff Software Engineer will lead the technical evolution of lending product platforms, collaborate with engineering teams, and mentor other engineers while enhancing software quality and system reliability.

Drive partnerships, manage sales cycles, prospect platforms, negotiate agreements, and collaborate with internal teams to enhance Parafin's partnership strategy.

As a Staff Product Engineer, you will lead technical direction for user-facing products, drive architecture decisions, and mentor engineers, ensuring high-quality user experiences across the stack.

Lead design, build, and maintenance of scalable data pipelines and lakehouse infrastructure using PySpark, Databricks, and Airflow. Own data & ML platform (Terraform, Databricks), improve observability, data quality, and developer tooling, and partner closely with Data Science and engineering teams.

The Partner Engineer role involves technical discovery, integration execution, and post-launch support for partnerships with various partners. You'll document processes, optimize integrations, and enhance partner experiences through effective communication and problem-solving.

Forward Deployed Engineers at Parafin drive complex partner launches and enhance scalable integrations, working with cross-functional teams and shipping core product improvements.

This role involves developing and maintaining ML platforms for model experimentation and deployment, improving systems, and ensuring quality and observability for ML-driven products.