Senior Software Engineer, ML Platform

About Us:

At Parafin were on a mission to grow small businesses.

Small businesses are the backbone of our economy but traditional banks often dont have their backs. We build tech that makes it simple for small businesses to access the financial tools they need through the platforms they already sell on.

We partner with companies like DoorDash Amazon Worldpay and Mindbody to offer fast and flexible funding spend management and savings tools to their small business users via a simple integration. Parafin takes on all the complexity of capital markets underwriting servicing compliance and customer service for our partners.

Were a tight-knit team of innovators hailing from Stripe Square Plaid Coinbase Robinhood CERN and more all united by a passion for building tools that help small businesses succeed. Parafin is backed by prominent venture capitalists including GIC Notable Capital Redpoint Ventures Ribbit Capital and Thrive Capital. Parafin is a Series C company and we have raised more than $194M in equity and $340M in debt facilities.

Join us in creating a future where every small business has the financial tools they need.

About The Position

Were looking for a software engineer to join Parafins 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 youll design build and maintain the core abstractions and platforms that let data scientists ship high-quality models to productionsafely and quickly. Youll partner closely with Data Science and Platform Engineering own the ML platform end-to-end and develop batch and real-time underwriting infrastructure.

What Youll 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.

What We Are 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 fundamentalsprobability & 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 stacksAWS 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.

• Strong problem-solving skills and a proactive attitude toward ownership and platform health.

• Excellent communication and collaboration skills especially in cross-functional settings.

Bonus Points

• Databricks experience (MLflow Model Serving).

• Experience with feature stores (e.g. Tecton Feast) and streaming (Kafka/Kinesis).

• Experience with fintech risk or underwriting systems; familiarity with model safety checks rejection/override flows and auditability.

• Background with A/B testing platforms shadow/canary deployments and automated rollback.

• Experience with low-latency inference systems.

What We Offer

• Salary Range: $230k - $265k

• Equity grant

• Medical dental & vision insurance

• Work from home flexibility

• Unlimited PTO

• Commuter benefits

• Free lunches

• Paid parental leave

• 401(k)

• Employee assistance program

If you require reasonable accommodation in completing this application interviewing completing any pre-employment testing or otherwise participating in the employee selection process please contact us.

Required Experience:

Senior IC