Staff Software Engineer, Internal Tools

Tubi - Canada•Toronto, ON

2d•CA$164,600 - CA$235,100•Hybrid

About The Position

We are looking for a Staff Engineer to steer the technical evolution of our AI-centric business platforms, which currently prioritize ad revenue operations such as cross-system intelligence, workflow automation, and document understanding. While the domain already supports live production traffic and active users, significant architectural challenges remain: how can non-deterministic AI be effectively integrated into workflows where business outcomes depend on high accuracy? Furthermore, how should system boundaries be structured during this phase of rapid platform integration and development? In this role, you will confront this ambiguity head-on—defining architectural patterns, implementing them in production, and validating their effectiveness. 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.

Requirements

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

Nice To Haves

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.

Responsibilities

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.