Software Engineer, AI/ML Infrastructure

As a Software Engineer on the ML Infrastructure team, you will work closely with product and platform engineering teams to build and evolve core AI platform capabilities. You will help design and improve systems that allow teams to develop, run, and scale GenAI-powered applications in production. Your work will directly support AI-driven experiences used by millions of customers and pros across the Thumbtack ecosystem.

Responsibilities

Build and evolve core AI platform capabilities that enable teams to develop, run, and scale GenAI-powered applications across Thumbtack; Contribute to the design, development, and deployment of scalable tools and infrastructure to support applied scientists, including traditional ML model training and serving systems, feature and data workflows, CI/CD, orchestration, deployment, and evaluation tooling; Work hands on across the stack, from backend services and execution infrastructure to integrations with AI models and tooling; Partner with senior engineers to evaluate next-generation AI infrastructure frameworks and tools; Drive projects to completion with a strong focus on business impact and measurable outcomes; Solve complex technical problems and stay up to date with advances in this rapidly evolving space

Requirements

1 to 3 years of professional software engineering experience; Strong fundamentals in data structures, algorithms, and software design; Proficiency in at least one major programming language, and the ability to work across multiple languages (primary stack includes Go and Python); Experience working with relational or NoSQL databases such as Postgres or DynamoDB; Demonstrated ability to use AI coding tools in day-to-day workflows and validate, critique, and refine AI-generated output; Comfort operating in a fast-paced environment with a certain level of ambiguity, especially when learning and picking up new technologies when projects require it; Operate with a bias to action, quickly learning what is needed and taking decisive steps to move work forward in ambiguous, fast-paced environments