# Senior Software Engineer, ML Platform

**Company:** [Parafin](https://hotfix.jobs/companies/parafin)
**Location:** San Francisco, CA
**Role:** ML Engineering
**Salary:** $230k – $265k/yr
**Experience:** 5+ years
**Skills:** Python, SQL, Pyspark, Spark, AWS, Databricks, Airflow, MLflow, Kubernetes, Kafka, Tecton, Feast
**Posted:** 2026-01-05

> Build and maintain scalable ML platform for model experimentation, training, evaluation, inference, and feature store to power underwriting products. Requires 5+ years experience with Python, ML stacks like Databricks/AWS, and MLOps systems.

## Job Description

## 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.

## 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).
- 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.

## What We Offer
**Salary Range: $230k - $265k**  
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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