Staff Software Engineer, Machine Learning Infrastructure

About the Role

Thumbtack helps millions of people confidently care for their homes by providing personalized guidance, AI tools, and a best-in-class hiring experience. Every day, homeowners rely on Thumbtack for urgent repairs, seasonal maintenance, and larger home improvement projects. If you're inspired by making an impact, join the Machine Learning Infrastructure Team to help shape the future of AI-driven experiences.

The Challenge

As a Staff Software Engineer on this team, you will be a senior technical leader responsible for critical AI-powered systems at Thumbtack. Your work will span two key areas:

• AI/ML Platform: Including LLM gateway, model training and serving, feature and data workflows, orchestration, and observability.
• AI Evaluation Platform: Including trace ingestion, scorer infrastructure, LLM-as-judge calibration, pre-deploy regression gates, and self-serve evaluation tooling for product teams.

This role is not about designing systems on a whiteboard—you will design, write, and ship significant systems yourself, raising the technical bar for the entire team. You will collaborate with applied scientists, product engineering teams, and leadership to define multi-quarter roadmaps, make critical build/buy/adopt decisions, and ensure the platform scales with Thumbtack’s rapidly growing AI ambitions.

What You’ll Do

• Build and evolve core AI platform capabilities to enable teams to develop, run, and scale GenAI-powered applications across Thumbtack.

• Contribute to the design, development, and deployment of scalable tools and infrastructure for 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.

• Evaluate next-generation AI infrastructure frameworks and tools to help product teams harness advances in AI.

• Drive projects to completion with a focus on business impact and measurable outcomes.

• Solve complex technical problems and stay updated with advances in this rapidly evolving space.

Requirements

• At least 10+ years of professional software engineering experience, with a track record of technical leadership at staff level, setting direction for systems spanning multiple teams, driving alignment, and delivering to production.

• 3+ years building AI/ML infrastructure in production, including model serving, feature platforms, training or orchestration systems, or ML observability.

• Hands-on experience with LLM-powered systems in production, including LLM gateways or proxies, prompt and model lifecycle management, RAG or agentic architectures, and cost optimization for LLM workloads.

• Experience building or operating AI evaluation systems, such as offline eval pipelines, LLM-as-judge approaches, regression testing for model or prompt changes, or A/B measurement of AI features.

• Deep experience designing, building, and operating reliable distributed systems, including ownership of production services at meaningful scale, on-call responsibility, and incident leadership.

• Demonstrated ability to use AI coding tools in day-to-day workflows and validate, critique, and refine AI-generated output.