About the job
About Us:
At Parafin, our mission is to empower small businesses to thrive in today's competitive landscape. We understand that small businesses form the backbone of our economy, yet they often face challenges in accessing essential financial resources. Our innovative technology streamlines access to vital financial tools directly on the platforms they already utilize for sales.
Partnering with industry leaders such as DoorDash, Amazon, Worldpay, and Mindbody, we provide small businesses with fast, flexible funding, efficient spend management, and effective savings solutions through simple integrations. Parafin manages the complexities of capital markets, underwriting, servicing, compliance, and customer support to ensure seamless experiences for our partners and their small business clients.
We are composed of a dynamic team of innovators with backgrounds from top firms like Stripe, Square, Plaid, Coinbase, Robinhood, and CERN, all driven by a passion for developing tools that facilitate small business success. Backed by esteemed venture capitalists including GIC, Notable Capital, Redpoint Ventures, Ribbit Capital, and Thrive Capital, Parafin stands as a Series C company with over $194M raised in equity and $340M in debt facilities. Join us in shaping a future where every small business has access to the financial tools they need.
About The Position
We are on the lookout for a skilled Software Engineer to join our Infrastructure team and spearhead the advancement of our Machine Learning (ML) Platform. This pivotal role is essential for constructing reliable, scalable, and developer-centric systems for model experimentation, training, evaluation, inference, and retraining that drive underwriting and other ML-powered products for small businesses.
As a Software Engineer, you will design, build, and maintain the core frameworks and platforms that empower data scientists to deploy high-quality models into production efficiently and safely. You'll work closely with Data Science and Platform Engineering, taking ownership of the ML platform from end-to-end, and develop both batch and real-time underwriting infrastructure.
What You'll Do
Transform notebooks into reliable software. Break down data scientist training and inference notebooks into reusable, well-tested components (libraries, pipelines, templates) with clear interfaces and documentation.
Develop user-friendly ML abstractions. Create SDKs, CLIs, and templates that simplify the definition of features, model training and evaluation, and deployment to batch or real-time targets with minimal boilerplate.
Construct our real-time ML inference platform. Establish and scale low-latency model serving capabilities.
Enhance batch ML inference processes. Optimize scheduling, parallelism, cost controls, and observability to improve efficiencies.
