Principal ML Engineer: Build & Deploy Production Models

Steadily; Austin (TX); On-site; USD 150,000 - 210,000; Full time

Benefits offered by this job

Top salary + equity

3 weeks PTO + 6 holidays

Health/Dental/Vision/Life/Disability/H

401(k)

Snacks & team meals

Job summary

Steadily is seeking a Principal Machine Learning Engineer in Austin, TX, to lead the ML lifecycle from data ingestion to production deployment. You’ll own data pipelines, feature engineering, and modeling while shaping the ML strategy for an early-stage scale-up.

The role emphasizes autonomy and business impact, with 4 days/week in-office. You will work across data types including image data, build scalable data layers, and collaborate with cross-functional teams to translate data insights into

Qualifications

• 5+ years experience applying ML methods to production problems.
• Ownership of end-to-end deployment of ML models is required.
• Ability to work from raw data sources through feature engineering to production models.

Responsibilities

• Own the end-to-end ML lifecycle: design, build, deploy, and evolve data sets and models with emphasis on scalability and quality.
• Build and maintain the data layer with lightweight pipelines and new dbt tables.
• Drive measurable business impact by exploring and deploying ML applications across the product ecosystem.
• Write clean, maintainable code in a stack using Kafka, AWS, Python, Django/FastAPI, and Postgres with CI/CD via GitHub Actions.
• Collaborate with Engineering, Product, Operations, and Business teams to deliver reliable solutions.
• Provide metrics on model quality, bias, and performance to demonstrate business value.

Skills

ML production experience

End-to-end ML deployment

Feature engineering

Data analysis

Python

SQL

Tools

Kafka

AWS

EKS

Postgres

dbt

GitHub Actions

Django/FastAPI

Job description

Steadily is seeking a Principal Machine Learning Engineer in Austin, TX, to lead the ML lifecycle from data ingestion to production deployment. You’ll own data pipelines, feature engineering, and modeling while shaping the ML strategy for an early-stage scale-up.

The role emphasizes autonomy and business impact, with 4 days/week in-office. You will work across data types including image data, build scalable data layers, and collaborate with cross-functional teams to translate data insights into