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 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).

Senior Software Engineer, ML Platform

Responsibilities

Qualification

Preferred

Benefits

Company

Parafin

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