About the Machine Learning Infrastructure Team

At Thumbtack, our challenges span a wide variety of areas, ranging from building search, ranking & recommendations systems to optimizing pricing and spam detection models. The ML Infrastructure team is responsible for centralizing, standardizing and evolving machine learning infrastructure capabilities for teams across engineering that experiment with or deploy machine learning models for different problems. To read more about some of the engineering challenges at Thumbtack, visit our engineering blog.

Challenge 

As a Senior Software Engineer focused on ML Infrastructure, you’ll focus on bringing the Thumbtack’s ML Infrastructure vision to life. You’ll collaborate with engineers, applied scientists and product managers across engineering to refine and execute on our shared vision for generative AI, feature platforms, model deployment, and model monitoring. This could include introducing the latest feature engineering & model building frameworks, creating a standardized process for model deployment and monitoring, working with the data platform team on evolving feature storage, or working with applied scientists on productization of complex models. 

Responsibilities

• Collaborate with engineers, applied scientists and product managers to identify shared ML infrastructure needs across areas like feature engineering, model experimentation, model inference & CI/CD, generative AI, and model monitoring.
• Build, maintain, and communicate our roadmap.
• Centralize and standardize ML infrastructure & associated best practices for product teams across engineering.
• When appropriate, experiment with and introduce next-generation ML infrastructure capabilities and frameworks so product teams can continue to harness the power of open source / vendor-driven advances in machine learning.
• Drive projects to completion with a tenacious focus on the business impact of those projects.
• Solve tough technical problems and stay up-to-date with the latest advances in this constantly evolving problem space.

What you’ll need

If you don't think you meet all of the criteria below but still are interested in the job, please apply. Nobody checks every box, and we're looking for someone excited to join the team.

• 5+ years of industry experience in engineering.
• 2+ years of industry experience working on machine learning modeling or infrastructure.
• You’re fluent in at least one major programming language and would be able to switch between multiple languages. In our stack, we use Go and Python most heavily.
• You have experience building software on top of relational databases such as Postgres or MySQL.
• You can break down complex problems rigorously and understand the tradeoffs necessary to deliver great, impactful products.
• You’re curious, you’re data-driven, you love to ask questions, and you think critically about problems.
• You love delivering value to your users and your teammates through your work.

Bonus points if you have

• You have experience building and evolving machine learning infrastructure.
• You have worked with frameworks like PyTorch, Tensorflow, Scikit-learn, and Airflow
• You have experience with generative AI infrastructure and vendors
• You have experience building and maintaining reliable, performant distributed systems.
• You’re familiar working with major cloud providers and/or the big data ecosystem (Amazon Web Services, Google Cloud Platform, Spark, etc).
• You’ve demonstrated your ability to thrive in a fast-paced startup environment.

Thumbtack is a virtual-first company, meaning you can live and work from any one of our approved locations across the United States, Canada or the Philippines.* Learn more about our virtual-first working model here.

For candidates living in San Francisco / Bay Area, New York City, or Seattle metros, the expected salary range for the role is currently $191,250 - $247,500. Actual offered salaries will vary and will be based on various factors, such as calibrated job level, qualifications, skills, competencies, and proficiency for the role.

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