This role reports to the Director of MLE and works closely with Engineering, Data Science, Product, and the Principal SRE. You will influence cross team platform standards and help elevate engineering rigor across ML and infrastructure. In addition to system design, you will mentor engineers on ML reliability, architecture decision making, and operational excellence. Own ML systems architecture Define ML lifecycle standards Push event driven ML integration Design model packaging and deployment strategy Introduce systemic improvements Reduce architectural and data debt Establish testing and QA standards across ML workflows Build a resilient, scalable ML platform that: Trains distributed models at scale Supports event driven feature computation Enables portable model deployment (internal + external) Standardizes ML lifecycle across products Aligns infrastructure to product usage patterns ML Platform Architecture Define and evolve training orchestration standards Batch vs. streaming inference strategy Feature store direction State store patterns and tooling CPU/GPU scaling strategy When to extend current tooling, and when to replace it Define and evolve training orchestration standards Batch vs. streaming inference strategy Feature store direction State store patterns and tooling CPU/GPU scaling strategy When to extend current tooling, and when to replace it Event Driven ML Integration Design feature pipelines as first-class ML system components Integrate queuing and event systems with ML workflows Build reactive retraining triggers Define model drift detection and automated response systems Ensure retraining pipelines are reproducible and fault tolerant Design feature pipelines as first-class ML system components Integrate queuing and event systems with ML workflows Build reactive retraining triggers Define model drift detection and automated response systems Ensure retraining pipelines are reproducible and fault tolerant Model Packaging & Distribution Define model artifact standardization Deterministic builds Dependency isolation Runtime configuration injection Security constraints Version compatibility contracts Define model artifact standardization Deterministic builds Dependency isolation Runtime configuration injection Security constraints Version compatibility contracts ML Observability, Testing & Reliability Standards Define model performance SLIs Drift detection frameworks Data freshness guarantees Latency SLOs Model failure modes Establish standards for: Automated testing of feature pipelines Training pipeline validation Model artifact verification CI/CD workflows for ML systems Safe promotion from experiment to production Work closely with the Principal SRE to integrate telemetry and operational standards across the full stack. Define model performance SLIs Drift detection frameworks Data freshness guarantees Latency SLOs Model failure modes Establish standards for: Automated testing of feature pipelines Training pipeline validation Model artifact verification CI/CD workflows for ML systems Safe promotion from experiment to production Work closely with the Principal SRE to integrate telemetry and operational standards across the full stack. Operational Excellence & On Call You will help define and operate a sustainable ML on call model in partnership with Engineering and SRE. This includes: Clear ownership boundaries between ML systems and infrastructure Incident classification and severity alignment Runbooks for model failures and data drift Postmortem processes focused on systemic improvement Reducing operational toil through automation You are comfortable being accountable for production ML systems, as well as designing systems that make firefighting rare. You will help define and operate a sustainable ML on call model in partnership with Engineering and SRE. This includes: Clear ownership boundaries between ML systems and infrastructure Incident classification and severity alignment Runbooks for model failures and data drift Postmortem processes focused on systemic improvement Reducing operational toil through automation You are comfortable being accountable for production ML systems, as well as designing systems that make firefighting rare. Reduce Data Architecture Debt Evaluate service landscape alignment to product usage Improve or redefine streaming feature architecture Reduce batch rigidity Recommend infrastructure simplifications Evaluate service landscape alignment to product usage Improve or redefine streaming feature architecture Reduce batch rigidity Recommend infrastructure simplifications