Software Engineer, AI/ML Infrastructure (Fully Remote, Worldwide)

Imagine powering the intelligent systems that help millions confidently manage their homes. Here at Thumbtack, we're building the one essential app for every homeowner, transforming how people discover and hire local service professionals across the entire U.S. From immediate repairs to major home improvements, our platform connects demand with a thriving community of 300,000 businesses. We're driven by the opportunity to make a massive impact, shaping decisions about what projects to tackle, when to do them, and who to trust. If you're a curious and innovative Software Engineer ready to dive into the core of AI and Machine Learning, this global, fully remote role with us offers an incredible chance to make a real difference.

Overview

Thumbtack is on a mission to simplify home care for everyone. Our platform serves as a vital bridge, connecting homeowners with skilled local professionals for everything from urgent fixes to ambitious renovations. We are a dynamic, impact-driven company, and our work touches lives daily in every corner of the United States.

The Machine Learning Infrastructure team sits at the heart of Thumbtack's intelligent systems. We face fascinating challenges across areas like search, personalized recommendations, effective matchmaking, dynamic pricing, robust safety measures, innovative content generation, and critical fraud detection. Our team is dedicated to centralizing, standardizing, and constantly evolving the AI and ML infrastructure that underpins all these experiences. We empower product engineering teams by providing them with scalable, high-performance systems crucial for driving AI innovation across the company. You can explore more about our engineering journey and specific challenges on our engineering blog.

Key Responsibilities

Design, develop, and maintain robust, scalable AI and Machine Learning platforms and services that power Thumbtack’s core products.
Build and enhance foundational infrastructure for the entire ML model lifecycle, including training, deployment, monitoring, and inference.
Implement and improve MLOps tools and pipelines, ensuring high reliability, reproducibility, and efficiency across our machine learning workflows.
Collaborate closely with ML engineers, data scientists, and other product teams to understand their needs and deliver innovative, effective infrastructure solutions.
Optimize existing systems for peak performance, cost efficiency, and an exceptional developer experience.
Contribute significantly to the architectural evolution and strategic roadmap of our entire ML ecosystem.
Ensure the reliability, availability, security, and compliance of critical ML infrastructure components.

Requirements

Strong experience as a Software Engineer, with a focus on building and maintaining backend or infrastructure systems.
Proficiency in at least one modern programming language such as Python, Java, or Go.
Hands-on experience with major cloud platforms such as AWS, GCP, or Azure.
Familiarity with distributed systems and technologies used for large-scale data processing.
A solid understanding of machine learning concepts, including model training, evaluation, and deployment patterns.
Experience with MLOps tools, frameworks, or practices is a significant advantage.
A Bachelor's degree in Computer Science or a related technical field, or equivalent practical industry experience.
Demonstrated strong problem-solving abilities and a proven track record of successful collaboration within technical teams.

What You'll Gain

The opportunity to work on high-impact projects that directly affect millions of users and local businesses across the U.S.
Significant professional growth in the rapidly evolving and critical domain of AI/ML infrastructure.
A highly collaborative, supportive, and truly remote-first work environment, connecting with talented individuals globally.
Direct influence on the core product experiences and strategic business outcomes at Thumbtack.
Challenging technical problems that push the boundaries of scalable AI innovation.