About the role
About The Position
We’re looking for a software engineer to join Parafin’s Infrastructure team and lead the evolution of our ML Platform. This role is critical to building reliable, scalable, and developer-friendly systems for model experimentation, training, evaluation, inference, and retraining that power underwriting and other ML-driven products for small businesses.
As a Software Engineer, you’ll design, build, and maintain the core abstractions and platforms that let data scientists ship high-quality models to production—safely and quickly. You’ll partner closely with Data Science and Platform Engineering, own the ML platform end-to-end, and develop batch and real-time underwriting infrastructure.
What You'll Do
- 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.
- Build our real-time ML inference platform. Stand up and scale low-latency model serving.
- Expand batch ML inference. Improve scheduling, parallelism, cost controls, observability, and failure/rollback for large-scale batch scoring and post-processing.
- Own and expand the feature store. Design offline/online feature definitions, high read/write throughput, and consistent offline/online semantics.
- Platform reliability and observability. Instrument training/inference for latency, throughput, accuracy, drift, data quality, and cost; build alerting and dashboards; drive incident response and postmortems.
- Underwriting infrastructure partnership. Support production batch and real-time underwriting systems in collaboration with Data Science; collaborate on model interfaces, SLAs, safety checks, and product integrations.
