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Salary: USD 215,000 - 275,000 / annual
Parafin is a Series C fintech company on a mission to help small businesses access financial tools through the platforms they already use. We partner with companies like DoorDash, Amazon, and Worldpay to embed capital and financial services. The company has raised $194M in equity and $340M in debt facilities, backed by top-tier VCs including GIC, Redpoint, Ribbit, and Thrive.
You'll join the Infrastructure team to lead the evolution of our ML Platform—a critical system powering underwriting and other ML-driven products for small businesses. This is an end-to-end ownership role where you'll design, build, and maintain core abstractions that enable data scientists to ship high-quality models to production safely and quickly.
Key responsibilities:
- Transform data scientist notebooks into production-grade software by decomposing training/inference code into reusable, tested components with clear interfaces.
- Build developer-friendly ML abstractions: SDKs, CLIs, and templates that simplify feature definition, model training/evaluation, and deployment to batch or real-time targets.
- Design and scale a low-latency real-time ML inference platform.
- Expand batch ML inference capabilities, improving scheduling, parallelism, cost controls, observability, and failure/rollback mechanisms.
- Own and expand the feature store, designing offline/online feature definitions with high throughput and consistent semantics.
- Instrument training/inference pipelines for latency, throughput, accuracy, drift, data quality, and cost; build alerting, dashboards, and drive incident response.
- Partner with Data Science and Platform Engineering on production underwriting systems, collaborating on model interfaces, SLAs, safety checks, and product integrations.
Required qualifications:
- 5+ years of software engineering experience, including ML platform/MLOps work (training, deployment, feature pipelines).
- Strong Python; solid software design and testing fundamentals; proficiency with SQL and hands-on Spark/PySpark.
- ML fundamentals knowledge: probability & statistics, supervised/unsupervised learning, bias/variance, feature engineering, model evaluation, validation strategies, and production concerns (drift, stability, monitoring).
- Expertise with modern data/ML stacks: AWS, Databricks (workflows, lakehouse, MLflow, 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, model registries, experiment tracking, and evaluation frameworks.
- Strong problem-solving, ownership mindset, and cross-functional collaboration skills.
Bonus: Databricks expertise, feature stores (Tecton, Feast), streaming (Kafka/Kinesis), fintech/underwriting experience, A/B testing platforms, low-latency inference systems.
About Parafin
Fintech — embedded capital and financial services for platforms serving small businesses.
