Hybrid Senior Software Engineer, ML Platform

at Parafin

• Job title: Senior Software Engineer, ML Platform
• Job type: Full Time
• Experience level: Senior
• Salary: $230,000 - $265,000 per year
• Degree requirement: Bachelor's Degree
• Tech skills: Airflow, AWS, PySpark, Python, Spark, SQL
• Location requirements: Hybrid, San Francisco, United States

About the role

• Senior Software Engineer for Parafin’s Infrastructure team, leading the evolution of the ML Platform and ensuring scalable, reliable systems for small business funding.

Responsibilities

• 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.

Requirements

• 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.
• Strong problem-solving skills and a proactive attitude toward ownership and platform health.
• Excellent communication and collaboration skills, especially in cross-functional settings.

Benefits

• Equity grant
• Medical, dental & vision insurance
• Work from home flexibility
• Unlimited PTO
• Commuter benefits
• Free lunches
• Paid parental leave
• 401(k)
• Employee assistance program