Senior Software Engineer

About the role

The role shapes how Homeward builds with AI by solving AI problems and creating tooling. The position connects internal partners, external customers, agents, and brokerages across multiple states. This role reports to the SVP, Technology and requires base in Austin, TX.

Software engineers turn product ideas into working code. Engineers work in small teams, review each other's work, and ship in small batches. Most teams follow agile practices such as sprints and daily standups. Engineers also write tests, fix bugs, and improve performance. The field values clear communication as much as technical skill. Engineers spend part of every week on planning, code review, and debugging, not just writing new code. The ability to explain a technical decision in plain words separates strong engineers from the rest.

Key facts

What you'll do

The role defines and communicates requirements while ensuring solutions meet user needs.

Solutions are prototyped rapidly and then refined with peers and senior engineers to become production-ready services. Shared tooling and reusable components are built to simplify LLM access, integration, and operation across technology teams.

AI coding assistants such as Claude and Copilot are used in daily workflows to increase team effectiveness.

Solutions are delivered in collaboration with Product, Engineering, and Data to ensure they are useful, well-documented, and easy to adopt. Hard, ambiguous problems solvable with LLMs and ML are tackled, and approaches are shared to help others solve similar issues.

Requirements

You have 4-6 years of professional back-end development experience, ideally in a fast-moving environment. Hands-on experience building or operating AI or ML systems in production is required, with a focus on reliability, performance, cost, and safety.

You have production experience with LLM APIs, including prompt design and context management. Proficiency in Python and a back-end framework such as Django, Flask, or FastAPI is required. Understanding of REST APIs, asynchronous task management, and sound data management is required.

Strong judgment on trade-offs between reliability, latency, cost, quality, and maintainability is exercised, and timely decisions are made. Incredibly strong ownership of delivering high-quality solutions is demonstrated. Clear written and verbal communication skills are used to make complex topics understandable to others.

A build, test, learn mindset is embraced with a bias toward action and a focus on business outcomes. Flexibility is shown through tasks ranging from updating an email template to building complex web applications as the team needs. General curiosity is maintained while navigating a fast-changing AI landscape. Familiarity with version control and CI/CD tools is required.

Practical notes

Candidates must be based in Austin, TX. Typical interview steps

Hiring for engineering roles usually starts with a recruiter screen, followed by one or two technical rounds. Candidates often solve a coding problem, discuss past projects, and answer system design questions. Some loops include a take-home task. Final rounds typically cover team fit and give candidates a chance to ask questions. Interviewers look for how you break down an unfamiliar problem, not just whether you reach the answer. Practicing a few problems aloud and reviewing your own past projects are the best preparation.

Good to know

Applied AI roles often combine software engineering with machine learning workflows. Modern AI products rely heavily on LLM API integrations, prompt design, and context management. Teams typically use Python back-end frameworks to serve models and manage asynchronous tasks. Reliable observability and evaluation practices are essential for production AI systems. Strong written communication is critical when working with cross-functional stakeholders in fast-paced environments.