Staff Machine Learning Engineer, Unity

• Experience in machine learning with a track record of owning production ML systems end to end, from data to serving to measured business outcome
• Deep experience in performance marketing or ad-tech modeling: predicting acquisition cost, lifetime value, return on ad spend, retention or comparable outcomes
• Strong grounding in probabilistic prediction and calibration: quantile or interval prediction, coverage, bias correction and backtesting of forecasts
• Experience building LLM-based agent systems in production: orchestration, evaluation harnesses, prompt and model versioning, and cost control
• Strong Python and modern ML tooling, plus solid data systems fundamentals across a relational store, a columnar analytics store and an event stream
• Proven ability to drive technical direction across teams, mentor senior engineers, and influence decisions without formal authority
• Comfort building in a zero-to-one environment

Desirable

• Set the technical vision and architecture for our ML platform, from data ingestion, semantic layer and feature stores through model registry, evaluation and serving
• Make the calls that keep every model output reproducible and auditable: versioned data snapshots, model and prompt versions, and cost tracked per decision
• Define, lead, and spearhead engineering practices for the agentic era, including agentic development workflows and evaluation methodologies
• Mentor engineers from different backgrounds
• Partner with different functional teams and disciplines to translate expert human judgment into training signals. Shape the roadmap with the product team
• Build predictors of commercial outcomes such as user acquisition cost, lifetime value and return on ad spend from large, noisy, multi-source signals
• Build forecasting models over market signals that detect emerging demand and estimate when a window opens, peaks and closes
• Deliver every prediction as a calibrated range with stated confidence, and own the ranking and portfolio logic that turns predictions into recommendations within a budget
• Design online experimentation with guardrails, safe exploration and clear human decision points
• Build evaluation harnesses for LLM-based agents, including automated playtesting and multimodal assessment of generated content
• Orchestrate frontier-model agents in production with strict data boundaries and cost control
• Own the feedback loop that scores predictions against realized outcomes and corrects bias, coverage and signal weighting over time
• Make the system report its own error in plain language so that creators and internal stakeholders can trust it