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Parafin Logo Parafin · IT - Software Development

Senior Software Engineer, ML Platform

United States, San Francisco 230000.00 - 265000.00 USD / Year · Job Posted February 21, 2026
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Job Description

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.

Job Responsibility

  • 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

Nice to have

  • 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

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