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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Software Engineer - **Company:** Parafin Inc - **Location:** San Francisco, CA, United States (Remote available) - **Experience:** Expert - **Salary:** $220,000.0 - $265,000.0 - **Contract:** Permanent contract - **Skills:** A/B Testing, Airflow, Amazon Web Services, Python (Programming Language), Systems Development Life Cycle, Azure Machine Learning, Service Design, Software Engineering, SQL Databases, Data Streaming, Scripting, Real Time Systems, Feature Engineering, System Availability, Apache Spark, Model Validation, Caching, Rate Limiting, Pyspark, Apache Kafka, Machine Learning Operations, SDET, Unsupervised Learning, Databricks - **Published:** July 31, 2026 - **Apply:** https://www.careerbuilder.com/job-details/senior-software-engineer-ml-platform-san-francisco-ca--373af9b4-128c-4ce3-baea-ac38ed16bc65 ## About the Role * 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. 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., Business Growth, Caching, Capital Markets, Communication Skills, Cost Control, Cross-Functional, Customer Support/Service, Data Science, Dental Insurance, Employee Assistance Plan, Incident Response, Metrics, Model Review, Problem Solving Skills, Product Safety, Production Support, Python Programming/Scripting Language, Reporting Dashboards, Scalable System Development, Service Level Agreement (SLA), Small Business, Software Design for Test (SDET), Software Engineering, Statistics, Team Player, Traffic Shaping, Underwriting, Venture Capital, Vision Plan ## Description 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. What You'll 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. ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) - [Empowering Retail Through Applied Machine Learning](https://www.wearedevelopers.com/videos/976-empowering-retail-through-applied-machine-learning) - [Shipping Faster with Less: Render on Cloud Hosting, AI Workloads, and the Future of DevOps](https://www.wearedevelopers.com/videos/1894-shipping-faster-with-less-render-on-cloud-hosting-ai-workloads-and-the-future-of-devops) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) ## Related Articles - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers) - [Dev Digest 137 - AI'm not sure about this](https://www.wearedevelopers.com/magazine/485-dev-digest-137-ai-m-not-sure-about-this) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Why Upskilling And Reskilling is Important For Developers](https://www.wearedevelopers.com/magazine/428-why-upskilling-and-reskilling-is-important-for-developers)