Staff Software Engineer (Internal Tools)

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., * Define and drive the technical architecture of the AI platform - spanning extraction pipelines, confidence scoring, cross-system data aggregation, and user-facing intelligence layers. Ensure systems are modular, testable, and evolvable.

• Own the platform’s most complex subsystems and hardest design problems: reliability under non-determinism, integration across heterogeneous data sources, and progressive automation with appropriate human-in-loop gates.
• Establish clear system boundaries and integration contracts with adjacent platforms. Design interfaces that are resilient to schema evolution and organizational change.
• Ship consistently. Turn ambiguous requirements into working software that users see and trust. Bias toward iteration and measurable progress over extended design phases.
• Mature the platform’s AI systems from early-stage to production-grade: systematic validation, feedback loops that improve accuracy over time, and principled cost management.
• Raise the engineering bar through code review, design discussions, and pairing. Provide clear technical analysis when leadership needs to understand trade-offs, feasibility, or risk., * Trust Engineering: Build systems where human operators can progressively delegate to AI - and where the blast radius is contained when AI is wrong.
• AI as Leverage: Think about AI as a force multiplier for a small team. The systems you build should measurably expand what the team can accomplish.
• Cost Discipline: Treat AI spend as an engineering problem - optimizing for accuracy per dollar, not just accuracy alone.
• Pragmatic Adoption: Evaluate new capabilities with a builder’s eye - what to use now, what’s hype, what to wait for.

• 8+ years of software engineering experience, with demonstrated ability to own and deliver complex systems end-to-end - from design through production operation.
• Deep expertise in distributed systems design, fault tolerance, and building reliable systems from unreliable components.
• Strong full-stack engineering fundamentals. Our stack: Node.js/TypeScript (backend, Express), React/TypeScript (frontend), PostgreSQL with Drizzle ORM, Python for ML pipeline components, OpenAI and Google Gemini APIs.
• Demonstrated bias toward shipping. Track record of turning ambiguous problems into working systems on a reasonable timeline - not just designs or proposals.
• Good technical judgment under uncertainty. Pragmatic trade-offs, knowing when “good enough” is right, and not over-engineering when the problem doesn’t call for it., * Experience designing systems that handle non-deterministic or probabilistic outputs (ML models, LLM pipelines, or similar) with appropriate confidence scoring and fallback strategies.
• Familiarity with LLM application patterns: prompt engineering, multi-model orchestration, agentic tool use, and cost optimization for high-volume LLM workloads.
• Experience with document AI or structured data extraction from unstructured sources (documents, image, natural language) at scale.
• Background in ad tech, revenue operations, or media operations - order management, pricing logic, deal lifecycle.
• Experience building over heterogeneous enterprise data sources with varying freshness, schemas, and access patterns.

• Pay range: $227,200 - $324,500 USD (annually).
• Eligible for an annual discretionary bonus, long-term incentive plan, and various benefits including medical, dental, vision, insurance, a 401(k) plan, paid time off.
• High-cost labor markets such as Los Angeles, New York City, and San Francisco are included.