Staff Machine Learning Engineer

Steadily; Austin (TX); On-site; USD 180,000 - 240,000; Full time

Benefits offered by this job

Top of market salary + equity

3 weeks PTO + 6 federal holidays

Medical, dental, vision, life, disable

401(k)

Free snacks

Team lunches

Collaborative office culture

Job summary

Steadily in Austin, TX is seeking a Staff Machine Learning Engineer to own end-to-end ML projects—data prep, model development, deployment, and monitoring in production. As the second ML hire, you will influence architecture, tooling, and ML strategy.

You will work with raw data sources, build lightweight pipelines, perform feature engineering, and deploy models across a scalable, event-driven stack (Kafka, AWS, Python, Django/FastAPI, Postgres).

Qualifications

• 5+ years experience applying ML methods to production problems.
• Ability to dive into complex codebases and own end-to-end deployment.
• Experience building data pipelines and feature engineering for ML.

Responsibilities

• Own end-to-end ML lifecycle: design, build, deploy, and monitor models.
• Build lightweight data pipelines and dbt tables for model-ready data.
• Drive measurable business impact with ML applications in product ecosystem.
• Write clean, maintainable code in an event-driven stack (Kafka, AWS, Python, Django/FastAPI, Postgres).
• Collaborate with Engineering, Product, Operations, and Business teams.

Skills

Machine Learning

End-to-end ML

Feature Engineering

Python

Kafka

AWS (EKS)

Django/FastAPI

Postgres

GitHub Actions

dbt

Tools

dbt

Kafka

AWS EKS

Django

FastAPI

Job description

Overview

Location: Austin, TX (4 days in-office)

Employment Type: Full-time

Department: Engineering & Product

As a Staff Machine Learning Engineer:

You will play a key technical role on our Engineering team, identifying trends and insights across large data sets to discover where refined data or internal ML/AI models can improve our product outcomes and operations.

As the second engineer joining our dedicated ML team, you will have outsized influence on our architecture, tooling, and ML strategy. Because we are a fast-growing, agile company, this is a true end-to-end role. You’ll own researching, building, evaluating, and deploying your models to production, as well as monitoring them for quality and accuracy over time. We operate across data types including public, proprietary, and a large volume of image data.

Because we currently operate without a dedicated Data Engineering team, you will also own the data layer for your models. In practice, that means you'll often be the first person to touch a given raw data source; you're comfortable going from an unrefined, previously unexplored data set through feature engineering and into a production ML model. You can expect roughly a 30% data pipeline / new dbt table building (lightweight, not heavy ETL) and 70% feature engineering, modeling, deployment, and monitoring split in your day-to-day work.

You’ll operate with a high degree of autonomy and serve as a trusted technical owner for business problems across the organization. Steadily is still early in our exploration of where AI/ML models can drive the biggest value, making this role ideal for engineers who thrive in ambiguous environments and want their technical work to translate directly into massive business impact.

This is a full-time position based in our Austin, TX office (4 days a week in-office).

Job Responsibilities

• Own the end-to-end ML lifecycle: Design, build, deploy, and evolve data sets and models with an emphasis on scalability, quality, and maintainability. Focus areas could include estimating property-level risk, accurately assessing costs, and using aerial image analysis or modeling techniques to identify attributes that feed into other models.

• Build and maintain the data layer: Build lightweight data pipelines and new dbt tables to get raw data model-ready, without owning heavy ETL infrastructure.

• Drive measurable business impact: Lead the exploration and implementation of new ML applications in our product ecosystem to better predict risk on a per-insured level and in aggregate across the entire portfolio.

• Write clean, maintainable code in our stack: We build on an event-driven architecture using Kafka, AWS (EKS), Python, Django/FastAPI, and Postgres, with a full CI/CD pipeline via GitHub Actions. You will set a high bar for engineering quality and architectural design within this ecosystem.

• Partner closely with Engineering, Product, Operations, and Business teams to design reliable solutions across systems and ensure your models are solving real-world problems.

• Provide excellent metrics and visibility into model quality, bias, and performance to assess how it’s helping the business, ensuring a high bar of scientific rigor and evaluation.

What we’re looking for:

• Experienced: 5+ years experience applying Machine Learning methods to production problems. We expect you to be able to dive into a complex codebase without too much spin-up. Past experience as a team lead or owning end-to-end deployment is definitely a plus.

• Full-stack with data: You're comfortable starting from a raw, unrefined data source that no one has previously worked with, building the lightweight pipeline or dbt table to make it usable, and carrying it all the way through feature engineering, modeling, and deployment.

• Builder with a Business Mindset: You like the product-side of data and think about how to apply modeling and evaluation techniques to real-world problems. You aren't just interested in the research; you have thoughtful opinions about where the data leads and how to maximize the business impact of your work.

• Pragmatic: We prioritize impact and delivery. You balance speed and quality, making thoughtful trade-offs to solve problems effectively. You leverage off-the-shelf solutions (and foundational models) so we don’t reinvent the wheel, but you understand when a custom solution is appropriate.

• Curious: You are not just an order-taker. You are curious about what makes the business tick and you learn the intricacies of how it runs. This results in strong intuition for when an analysis is wrong and leads you to suggest ideas and insights that nobody thought to ask for. You’re not the type of engineer who wants fully fleshed-out specs thrown over the wall for you to implement.

Nice to have:

• Actuarial experience, or experience applying models to risk evaluation and aggregation problems.

• Experience in computer vision and image analysis.

• Experience with dbt or similar modern data transformation tools.

Additional Information:

• Applicants must be authorized to work in the United States. We are unable to provide visa sponsorship at this time.

What We Offer:

• Compensation: Top of market salary + equity

• Time Off: 3 weeks PTO + 6 federal holidays

• Insurance: Medical, dental, vision, life, disability, HSA, FSA

• Retirement: 401(k)

• Perks: Free snacks, team lunches, collaborative office culture

Why Join Steadily:

• Good company. Our founders have three successful startups under their belt and have recruited a stellar team to match.

• Top compensation. We pay at the top of the Austin market (see comp).

• Growth opportunity: We’re an early-stage, fast-growing company where you’ll wear a lot of hats and shape product decisions.

• Strong backing. We’re growing fast, we manage over $20 billion in risk, and we’re exceptionally well‑funded.

• Culture: Steadily boasts a very unique culture that our teammates love. We call it like we see it and we’re nothing if not candid. Plus, we love to have a good time. Check out our culture deck to learn what we’re all about.

• Awards We’ve been recognized both locally and nationally as a top place to work. Recently we were ranked 16th on Forbes' 2026 Best Startup Employers list, and 63rd on the prestigious Inc 5000 Fastest Growing Companies list. We’ve also been recognized as one of the Best Landlord Insurance Companies in 2026 by CNBC, a Top 2025 Startup in Newsweek, in Investopedia's Best Landlord Insurance Companies, and we won Austin Business Journal's Best Places to Work in 2025.

We’re excited to meet you!