Senior Software Engineer, ML Platform

🕒 January 5

🏢🏡 San Francisco – Hybrid

💵 $230k - $265k / year

⏰ Full Time

🟠 Senior

🧑‍💻 Full-stack Engineer

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Parafin

WebsiteLinkedInAll Job Openings

51 - 200 employees

💳 Fintech

💸 Finance

🤝 B2B

Fintech • Finance • B2B

Parafin is a financial technology company that provides ready-to-launch financial products to help merchants grow. It partners with various platforms, from marketplaces to SaaS and payment processors, offering services such as capital and spend management through a powerful financial services infrastructure. Parafin handles underwriting, disbursement, and servicing, offering low-lift solutions with no-code implementation, eliminating credit or compliance risk for its partners.

📋 Description

• 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