# Senior Software Engineer, ML Platform at Parafin

- Company: Parafin
- What the company does: Parafin partners with platforms to deliver seamless, flexible access to financing, giving small businesses the capital they need to grow with confidence. Backed by Redpoint.
- Company website: https://www.parafin.com/
- Type: Startups (AI role)
- Level: Senior
- Location: San Francisco, CA
- Work setup: Remote
- Pay: $215K to $275K base salary per year (USD)
- Posted: 2026-08-13
- Apply by: 2026-10-08
- Apply: https://jobs.ashbyhq.com/parafin/fd135431-5f66-4977-bf0d-1fb007b5cf89
- Page: https://www.1752.vc/careers/jobs/parafin-senior-software-engineer-ml-platform/

## About the role

Turn notebooks into software. Decompose data scientist training/inference notebooks into reusable, tested components (libraries, pipelines, templates) with clear interfaces and documentation. Create developer-friendly ML abstractions. Build SDKs, CLIs, and templates that make it simple to define features, train/evaluate models, and deploy to batch or real-time targets with minimal boilerplate.

## What they're looking for

- 5+ years of software engineering experience, including experience on ML platform/MLOps systems (training, deployment, and/or feature pipelines)
- Strong Python, solid software design and testing fundamentals. Proficiency with SQL, hands-on Spark/PySpark experience
- Knowledge of ML fundamentals—probability & statistics, supervised vs. unsupervised learning, bias/variance & regularization, feature engineering, model evaluation metrics, validation strategies, and production concerns like drift, stability, and monitoring
- Expertise with modern data/ML stacks—AWS, Databricks (workflows, lakehouse, MLflow/registry, Model Serving), and Airflow (or equivalent orchestration)
- Experience building real-time systems (service design, caching, rate limiting, backpressure) and batch pipelines at scale
- Practical knowledge of feature-store concepts (offline/online stores, backfills, point-in-time correctness), model registries, experiment tracking, and evaluation frameworks

Tags: Engineering