Staff ML Engineer — End-to-End Production ML in Austin

Steadily; Austin, Northern (TX, KY); Hybrid; USD 140,000 - 210,000; Full time

Benefits offered by this job

Top of market salary + equity

3 weeks PTO + 6 federal holidays

Medical, dental, vision, life, HSA, F/

401(k) retirement plan

Free snacks

Job summary

Steadily in Austin, TX is seeking a Staff Machine Learning Engineer to own end-to-end ML lifecycle—from data collection to production deployment—across image and other data types. You’ll shape architecture, tooling, and ML strategy as the second ML hire, with significant autonomy and business impact.

You will touch data engineering, feature engineering, modeling, deployment, and monitoring in a fast-growing, in-office 4-days-per-week environment, reporting to the Engineering & Product teams.

Qualifications

• 5+ years applying ML methods to production systems.
• Ability to own end-to-end deployment of models.
• Strong Python and data engineering fundamentals.
• Experience with image data and ML tooling is a plus.

Responsibilities

• Own end-to-end ML lifecycle from data to production.
• Build lightweight data pipelines and dbt tables.
• Drive ML applications to improve product outcomes.
• Write clean code in Kafka/AWS/Python/Django/FastAPI/Postgres.
• Collaborate with Engineering, Product, Operations and Business teams.
• Provide metrics on model quality, bias, and performance.

Skills

Production ML

End-to-end ML

Python

Data-driven decisions

Tools

Kafka

AWS

PostgreSQL

dbt

GitHub Actions

Job description

Steadily in Austin, TX is seeking a Staff Machine Learning Engineer to own end-to-end ML lifecycle—from data collection to production deployment—across image and other data types. You’ll shape architecture, tooling, and ML strategy as the second ML hire, with significant autonomy and business impact.

You will touch data engineering, feature engineering, modeling, deployment, and monitoring in a fast-growing, in-office 4-days-per-week environment, reporting to the Engineering & Product teams.