Senior ML Engineer — End-to-End AI in Production & Data

Steadily Insurance Company; Austin (TX); Hybrid; USD 215,000 - 275,000; Full time

Benefits offered by this job

Top of market salary + equity

3 weeks PTO + 6 federal holidays

Medical, dental, vision, life, etc.

401(k)

Job summary

Steadily Insurance Company in Austin, Texas is seeking a Senior Machine Learning Engineer to own the end-to-end ML lifecycle—from data prep to production deployment. You’ll work with diverse data types, including image data, and help shape ML strategy and architecture with autonomy.

You will build lightweight data pipelines, perform feature engineering, and deploy models while monitoring quality and bias. Collaboration across Engineering, Product, Operations, and Business is essential.

Qualifications

• 5+ years experience applying ML methods to production problems.
• Experience with end-to-end deployment of ML solutions.
• Ability to build lightweight data pipelines and feature engineering workflows.
• Hands-on with model evaluation, monitoring, and production governance.

Responsibilities

• Own the end-to-end ML lifecycle: design, build, deploy, and evolve data sets and models.
• Build and maintain the data layer with lightweight pipelines and new dbt tables.
• Drive measurable business impact by exploring new ML applications in the product ecosystem.
• Write clean, maintainable code in a modern stack (Kafka, AWS, Python, Django/FastAPI, Postgres).
• Partner with Engineering, Product, Operations, and Business teams to design reliable solutions.
• Provide metrics on model quality, bias, and performance to guide decisions.

Skills

Machine Learning in production

End-to-end ML deployment

Feature engineering

Data pipeline modeling

Business-minded data

Tools

Kafka

AWS (EKS)

Python

Django/FastAPI

Postgres

Job description

Steadily Insurance Company in Austin, Texas is seeking a Senior Machine Learning Engineer to own the end-to-end ML lifecycle—from data prep to production deployment. You’ll work with diverse data types, including image data, and help shape ML strategy and architecture with autonomy.

You will build lightweight data pipelines, perform feature engineering, and deploy models while monitoring quality and bias. Collaboration across Engineering, Product, Operations, and Business is essential.