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Senior Machine Learning Engineer

Steadily

Austin, TXDataFull-time
Before you apply

Role details

The practical picture
Employment
Full-time
Role signal brief

The role, at a glance

Extracted from the source posting · 90% confidence

Senior Machine Learning Engineer at Steadily (Austin, TX) with 4 days in-office per week. The role is end-to-end ML ownership including data layer building, feature engineering, model deployment, and monitoring. Focus areas include property risk estimation, cost assessment, and aerial image analysis for insurance applications. No dedicated Data Engineering team exists, so the engineer will own data pipelines and dbt tables. Must work in an on-site hybrid model with high autonomy.

What you need

  • 5+ years experience applying Machine Learning methods to production problems
  • Full-stack with data: comfortable building from raw unrefined data sources through pipelines, feature engineering, modeling, and deployment
  • Builder with a Business Mindset: applies modeling to real-world problems and thinks about business impact
  • Pragmatic: balances speed and quality, leverages off-the-shelf solutions including foundational models
  • Curious and autonomous: not an order-taker, owns end-to-end delivery

What you will own

  • Own the end-to-end ML lifecycle: design, build, deploy, and evolve data sets and models with emphasis on scalability, quality, and maintainability; focus areas include estimating property-level risk, assessing costs, and aerial image analysis
  • 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 exploration and implementation of new ML applications in the product ecosystem to better predict risk on a per-insured level and in aggregate across the entire portfolio
  • Write clean, maintainable code in Python/Kafka/AWS/EKS/Django/FastAPI/Postgres ecosystem with GitHub Actions CI/CD
  • Partner closely with Engineering, Product, Operations, and Business teams to design reliable solutions across systems
  • Provide excellent metrics and visibility into model quality, bias, and performance
Core skills
Machine LearningPythonKafkaAWS (EKS)PostgreSQLdbtFastAPIDjangoCI/CDGitHub Actions
Internal freshness window ends Oct 22, 2026
View source evidence
  • location “Austin, TX (4 days in-office)”
  • seniority “Senior Machine Learning Engineer”
  • employment_type “Full-time”
  • 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”
  • responsibilities “Because we currently operate without a dedicated Data Engineering team, you will also own the data layer for your models”
  • must_have “5+ years experience applying Machine Learning methods to production problems”
  • must_have “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”
  • must_have “You balance speed and quality, making thoughtful trade-offs to solve problems effectively”
machine learningpythonkafkaaws (eks)
Role brief

Overview

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

Employment Type: Full-time

Department: Engineering & Product

As a Senior 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 add input 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 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). We are located near Mopac and W. Anderson Lane.

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 with the help of AI: 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.

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:

We’re excited to meet you!