Senior Software Engineer, Demand Forecasting & Guidance
Amazon · Toronto, Ontario, CAN ·
- Seniority
- Senior
- Category
- Software engineering
Live sports and Streaming TV are the fastest-growing premium inventory in advertising, and they are the hardest to forecast. A regular-season game and a playoff elimination game are the same length and the same ad load, but they are not the same business. Amazon DSP's Live Events Optimizer (LEO) launched in December 2025 and is scaling from its first cohort of advertisers to hundreds; every one of them asks the same two questions before they commit budget - *how much inventory will there be, and when will it show up?* Answering those questions accurately is what our team builds.
We are looking for a Senior Software Development Engineer to own the systems that produce and serve those forecasts across Live Events and Streaming TV. This is a deep-systems role at the intersection of large-scale data engineering, real-time services, and applied ML. You will build the platform that ingests broadcast schedules and live game state from multiple third-party sources, resolves them into a canonical view of what is airing and when, and turns forecasted supply into signals that pacing and bidding act on.
You will work directly with applied scientists and data scientists who are pushing on the modeling side (e.g. tabular foundation models with LLM-derived semantic priors, contention and win-rate modeling, zero-shot forecasting for inventory with no history) and your job is to make that science real: productionized, monitored, cost-aware, and fast enough to matter during a live broadcast. Several of these systems are on the critical path for live inventory, so you will also raise the operational bar: telemetry, accuracy dashboards and guardrails that fail safely.
Key job responsibilities
- Own the architecture and delivery of large-scale, business-critical systems end to end - from ambiguous problem statement through design, implementation, launch, and long-term operation.
- Serve as the engineering counterpart to applied and data scientists: translate research prototypes into production systems, and shape modeling decisions early by making the engineering constraints and possibilities explicit.
- Build and own the platform layer that science depends on - data ingestion and feature pipelines, automated training and evaluation, online and batch inference, model deployment, and the monitoring that tells us when a model has quietly stopped working.
- Drive technical decisions that span multiple teams, resolving cross-team design disagreements and building alignment on approaches
- Raise the operational bar for the team: instrumentation and accuracy telemetry, operational readiness reviews, on-call quality, latency and cost efficiency at scale.
- Identify and pursue the highest-leverage engineering work in an area where the roadmap is still being written, and make the case for it with data.
- Mentor engineers and scientists on software design, testing, and production discipline; improve the team's engineering practices through code and design review.
- Represent the team's technical position to senior engineering, science and business leaders, and to partner teams across the advertising stack.
We are looking for a Senior Software Development Engineer to own the systems that produce and serve those forecasts across Live Events and Streaming TV. This is a deep-systems role at the intersection of large-scale data engineering, real-time services, and applied ML. You will build the platform that ingests broadcast schedules and live game state from multiple third-party sources, resolves them into a canonical view of what is airing and when, and turns forecasted supply into signals that pacing and bidding act on.
You will work directly with applied scientists and data scientists who are pushing on the modeling side (e.g. tabular foundation models with LLM-derived semantic priors, contention and win-rate modeling, zero-shot forecasting for inventory with no history) and your job is to make that science real: productionized, monitored, cost-aware, and fast enough to matter during a live broadcast. Several of these systems are on the critical path for live inventory, so you will also raise the operational bar: telemetry, accuracy dashboards and guardrails that fail safely.
Key job responsibilities
- Own the architecture and delivery of large-scale, business-critical systems end to end - from ambiguous problem statement through design, implementation, launch, and long-term operation.
- Serve as the engineering counterpart to applied and data scientists: translate research prototypes into production systems, and shape modeling decisions early by making the engineering constraints and possibilities explicit.
- Build and own the platform layer that science depends on - data ingestion and feature pipelines, automated training and evaluation, online and batch inference, model deployment, and the monitoring that tells us when a model has quietly stopped working.
- Drive technical decisions that span multiple teams, resolving cross-team design disagreements and building alignment on approaches
- Raise the operational bar for the team: instrumentation and accuracy telemetry, operational readiness reviews, on-call quality, latency and cost efficiency at scale.
- Identify and pursue the highest-leverage engineering work in an area where the roadmap is still being written, and make the case for it with data.
- Mentor engineers and scientists on software design, testing, and production discipline; improve the team's engineering practices through code and design review.
- Represent the team's technical position to senior engineering, science and business leaders, and to partner teams across the advertising stack.