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Software Engineer, AI/ML Infrastructure (US-Based)

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Remote from
USA
Salary
USD 123,300–188,100 / yr
Employment
Full Time
Experience
Senior
Published
Apply before
22 Nov 2026
Listing views
176
Application actions
12
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AI Summary

The role, at a glance.

Thumbtack is hiring a US-based Software Engineer to build and evolve AI/ML infrastructure used by product and platform teams. The role covers scalable model training and serving, data and feature workflows, CI/CD, orchestration, deployment, and evaluation tooling. The engineer will work hands-on across backend services, execution infrastructure, and AI model integrations, primarily using Go and Python. Success requires solid software-engineering fundamentals, familiarity with SQL or NoSQL data stores, effective use of AI coding tools, and comfort delivering in an ambiguous, fast-moving environment.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

4/5
EasyHard

Pace & Pressure

4/5
RelaxedFast-paced

Autonomy Level

4/5
GuidedFull ownership

Communication Load

4/5
IndependentCollaborative
AI insightThis is a technically demanding infrastructure role because it combines production software engineering with ML platform concerns such as model serving, orchestration, evaluation, and scalability. Candidates must learn evolving AI technologies quickly while coordinating closely with platform, product, and applied-science stakeholders.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job median (per year)Highly competitive
$155,700
US market range (per year)$120k–$180k
AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. Employer salary ranges are disclosed for US locations at $123,300–$188,100 USD. The annual cadence is inferred from the compensation scale and standard full-time US software-engineering pay conventions because the posting does not explicitly state a pay period. Estimated US market compensation for this AI/ML infrastructure software-engineering role is $120,000–$180,000 USD per year.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
How would you design a platform for deploying and serving multiple machine-learning models in production?

I would begin by defining model packaging, versioning, approval, deployment, rollback, and observability requirements. I would use a standardized model registry and deployment interface, isolate workloads appropriately, and provide autoscaling based on traffic and latency signals. The platform would track model version, input and output schemas, performance metrics, errors, and resource usage so teams can operate models safely.

Describe how you would make a feature or data workflow reliable and reproducible.

I would define explicit input and output contracts, version the code and relevant datasets or feature definitions, and orchestrate dependencies through an idempotent workflow. I would add data-quality checks, lineage metadata, retry policies, alerting, and clear failure handling. Reproducibility would also require controlled environments and automated tests for transformations.

When would you choose a relational database such as Postgres over a NoSQL database such as DynamoDB?

I would choose Postgres when the workload needs complex queries, joins, transactional consistency, and flexible relational reporting. DynamoDB is a stronger fit for predictable high-throughput access patterns, simple key-based reads and writes, and managed horizontal scaling. The decision should follow access patterns, consistency requirements, operational needs, and expected scale rather than a technology preference.

How do you validate code or technical output generated by AI coding tools?

I treat AI-generated output as an untrusted draft and validate its assumptions, security properties, correctness, and fit with existing architecture. I run unit and integration tests, inspect edge cases and error handling, review dependencies, and benchmark performance where relevant. I also make sure the result is understandable and maintainable before incorporating it into production code.

Tell us about your approach to delivering a project when requirements are ambiguous and technology is evolving quickly.

I first clarify the business outcome, constraints, stakeholders, and measurable success criteria. I then reduce uncertainty with a short technical investigation or prototype, document tradeoffs, and propose a phased delivery plan that creates value early. Throughout execution, I communicate risks and decisions frequently, use feedback to adjust scope, and keep ownership through launch and operational follow-up.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
Opportunity details

About this role.

Thumbtack helps millions of people confidently care for their homes.

Thumbtack is the one app you need to take care of and improve your home — from personalized guidance to AI tools and a best-in-class hiring experience. Every day in every county of the U.S., people turn to Thumbtack to complete urgent repairs, seasonal maintenance and bigger improvements. We help homeowners know which projects to do, when to do them and who to hire from our growing community of 300,000 local service businesses. If making an impact inspires you, join us. Imagine what we’ll build together.

About the Machine Learning Infrastructure Team

At Thumbtack, our challenges span a wide range of areas, including search, recommendations, matchmaking, pricing, safety, content generation, fraud detection, and more. The ML Infrastructure team is responsible for centralizing, standardizing and evolving AI/ML infrastructure that enables these experiences. We empower product engineering teams by providing scalable, high-performance systems that drive AI innovation at scale. To read more about some of the engineering challenges at Thumbtack, visit our engineering blog.

Challenge

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 AI-powered applications in production. Your work will directly support AI-driven experiences used by millions of customers and pros across the Thumbtack ecosystem.

What you’ll do

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

  • Contribute to the design, development, and deployment of scalable tools and infrastructure to support the efforts of our 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 that help product teams harness advances in AI.

  • 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.

In order to be successful, you must bring

  • At least 2 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. Our 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.

  • You operate with a bias to action, quickly learning what is needed and taking decisive steps to move work forward in ambiguous, fast-paced environments.

Expected salary ranges

  • For candidates living in San Francisco / Bay Area, San Jose, New York City, or Seattle metros, the expected salary range for the role is currently $145,400 – $188,100.

  • For candidates living in Austin, TX or Washington DC metros or in California, Massachusetts, New Jersey, or Washington states, the expected salary range for the role is currently $130,900 – $169,400.

  • For candidates living in all other US locations, the expected salary range for this role is currently $123,300 – $159,500.

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.

Thumbtack embraces diversity. We are proud to be an equal opportunity workplace and do not discriminate on the basis of sex, race, color, age, pregnancy, sexual orientation, gender identity or expression, religion, national origin, ancestry, citizenship, marital status, military or veteran status, genetic information, disability status, or any other characteristic protected by federal, provincial, state, or local law. We also will consider for employment qualified applicants with arrest and conviction records, consistent with applicable law.

Thumbtack is committed to working with and providing reasonable accommodation to individuals with disabilities. If you would like to request a reasonable accommodation for a medical condition or disability during any part of the application process, please contact: recruitingops@thumbtack.com.

For information about how Thumbtack collects, uses, and shares personal information about job applicants, please see our Job Applicant Privacy Policy.

We put as much craftsmanship into candidate safety as we do into the hiring experience itself. While scammers may try to impersonate our team, we’ll never ask you for money, banking info, or SSNs during hiring. Check out our blueprint on how to spot the fakes.

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This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content.

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