Senior Software Engineer, Internal Tools

About The Team

Tubi's Internal Tools team is at the forefront of AI integration, developing everything from developer resources to production-grade AI for business operations. We are the group responsible for turning AI from an experiment into an operating capability: training, infrastructure, developer agents, and AI-powered business systems. Engineers operate with high ownership and autonomy, collaborating on shared architectural decisions and AI infrastructure.

Tubi's Internal Tools team is at the forefront of AI integration, developing everything from developer resources to production-grade AI for business operations. We are the group responsible for turning AI from an experiment into an operating capability: training, infrastructure, developer agents, and AI-powered business systems. Engineers operate with high ownership and autonomy, collaborating on shared architectural decisions and AI infrastructure.

• Own systems end to end - design them, build them, and support them in production.
• Lead the projects you own: sequence the work, decide what lands first, and set technical direction for the engineers working with you.
• Sit with the people who use what you build, and turn what you learn there into a system.
• Design the service boundaries, contracts and schema evolution that let our platforms grow without breaking the teams depending on them.
• Make our AI systems dependable in production: evaluation harnesses, human approval steps before an agent acts, retries that handle a model returning something unexpected, and cost tracking that tells you what a task costs before you run it.
• Build what other engineers build on - agent skills, tool and MCP integrations, shared libraries, and raise the bar through code review, design discussion and mentoring.
• Spot the platform work nobody has asked for yet, make the case for it, and build it.

• 5+ years of professional experience building and operating production systems, from design through production ownership.
• A system you designed and can walk us through end to end - where its boundaries sit, what constrained it, and what you chose against.
• Strong programming proficiency in a statically typed language such as Rust, Go, C++, Java, Kotlin, C#, or Type Script. Production Rust is a plus rather than a requirement.
• You have owned a service in production: you wrote the runbooks, you knew what it cost, and you were the one paged when it broke.
• Experience designing systems that other teams integrate with - stable interfaces, schema evolution, and not breaking your consumers.
• Experience leading a project that other engineers contributed to, and communicating effectively with cross-functional partners.
• Proven ability to leverage AI-assisted development tools, with sound technical judgment when reviewing AI-generated output for correctness, security, and maintainability.
• Bachelor's or Master's degree in a technical field, or equivalent industry experience.

• Production Rust, or a systems background you are looking to apply to it.
• Data or measurement systems where getting the numbers right was the hard part.
• LLM and agent plumbing: tool and MCP integrations, evaluation harnesses, multi-model orchestration, cost control at volume.
• Internal platform or developer-experience work: systems other engineers build on rather than end-user features.

Tubi expects engineers at all levels to leverage AI tools (Claude Code, Codex, Cursor, MCP integrations) to accelerate delivery, testing, and documentation. On this team it goes further: much of what we build is the machinery that lets other people delegate their work to AI safely. You don't need to arrive as an AI expert, but you do need to develop deep fluency quickly and apply it with production-grade discipline:

• Trust Engineering:
  Building systems where people can progressively delegate to AI, and where the blast radius is contained when AI is wrong.
• AI as Leverage:
  Treating AI as a force multiplier for a small team, so that what you build measurably expands what the team can accomplish.
• Cost Discipline:
  Treating AI spend as an engineering problem - optimizing for accuracy per dollar, not accuracy alone.
• Pragmatic Adoption:
  Evaluating new capabilities with a builder's eye - what to use now, what's hype, and what to wait for.

Pursuant to local pay disclosure requirements, the pay range for this role, with final offer amount dependent on education, skills, experience, and location is as listed annually below.

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