Lead Machine Learning Operations Engineer

Storytelling is, and will always be, the heart of what we do at Skydance. Our vision is to build a thriving entertainment ecosystem where stories and worlds are imagined, brought to life and experienced. We create for every age and audience, anchored by the strength of our content and experiences and the depth of our connection with fans. We are creator-first, audience-focused, tech-forward, globally scaled. Here, you’ll work with talented teams that move fast, think boldly and collaborate with purpose to deliver results. We aim high, champion the best ideas and win together, earning the trust of our employees, creators, partners and audiences every day. If you’re energized by the opportunity to help build the next-generation global media and entertainment company powered by creativity and technology, we’d be thrilled to welcome you aboard. LET’S GO! Lead Machine Learning Operations Engineer Personalization & Recommendation Systems Overview We’re hiring a Lead Machine Learning Operations Engineer to own the operational excellence, observability, reliability, and governance layer around our personalization and recommendation ML systems. Our recommendation models retrain and deploy frequently. You will define how we detect model behavior changes, diagnose issues quickly, and prevent bad deployments from reaching customers. This is a lead-level IC role: you’ll set technical direction and drive adoption across ML Engineering, DevOps, Platform Engineering, Data Engineering, and Product. Sitting within ML Platform and Infrastructure, you’ll partner closely with ML engineers who own model development. You’re not expected to build infrastructure from scratch, but you’ll define what good looks like, evaluate tooling, and own the day-to-day operational layer. What You’ll Do Own ML production reliability strategy • Define and lead the operational strategy for production ML systems, including monitoring, traceability, deployment safety, incident response, and post-deployment validation. • Set the standards ML teams use to assess model health, performance, and trustworthiness in production. Own model traceability and governance • Ensure every production model has clear lineage (data, features, code, artifacts, validation, deployment history) and drive adoption of model registry and metadata tooling across ML teams. Build end-to-end ML observability • Design and implement monitoring across the full ML signal path: data arrival, feature freshness, distribution stability, candidate generation, ranking behavior, model metrics, serving latency, and SLA performance. Define production health metrics • Partner with ML, data, product, and business stakeholders to define post-deployment metrics covering model quality, system reliability, business guardrails, and degradation indicators. Detect drift and degradation proactively • Detect data drift, feature drift, model behavior changes, and silent failures before they impact customers via thresholding, alerting, anomaly detection, and release-over-release monitoring. Lead diagnostic tooling and root-cause analysis • Build dashboards, logs, and diagnostic workflows that progress quickly from “recommendations look off” to root cause, with context captured across candidates, features, scores, ranking decisions, and downstream outcomes. Own ML deployment safety • Define and operate automated gates that prevent bad models or bad data from being promoted to production. Partner with MLEs to establish validation checks, rollback criteria, canary strategies, shadow testing, and release health reviews. Lead ML incident response • Own incident response practices for ML systems, including rollback playbooks, hotfix strategies, severity definitions, tradeoff frameworks, communications, and post-mortems. Drive closure of systemic gaps after incidents rather than only resolving the immediate issue. Partner across ML Platform, Data, and ML • Partner with DevOps/Platform on infrastructure and observability needs; with Data Engineering on data quality, drift, and freshness; and with ML Engineering to embed operational requirements into development and deployment workflows. Set standards and mentor others • Act as the technical lead for ML operations: establish reusable patterns, playbooks, and standards, and mentor engineers on reliability, observability, and operational rigor. Basic Qualifications • 5+ years of experience in machine learning engineering, ML platform, applied ML, MLOps, data platform, reliability engineering, or a related technical role. • Demonstrated experience operating production ML systems, including monitoring, deployment, incident response, model validation, data quality, or reliability ownership. • Experience leading technical initiatives across multiple engineering teams, especially where success required influencing architecture, tooling, standards, or adoption. • Hands-on experience with model registries, feature stores, ML metadata systems, production monitoring, model deployment pipelines, or ML observability platforms. • Solid knowledge of end-to-end ML systems, including training data, features, model artifacts, offline validation, online serving, post-deployment metrics, and business outcome measurement. • Ability to reason about ML operational failure modes: stale features, distribution shift, training-serving skew, delayed labels, and offline-online metric gaps. • Solid SQL skills and comfort investigating data quality, feature distributions, model outputs, pipeline behavior, and production anomalies. • Track record of cross-functional collaboration with Platform, Data, and ML Engineering to deliver production-grade operational capabilities. • Solid written and verbal communication skills, including the ability to explain ML system health, risks, incidents, and tradeoffs to both technical and non-technical stakeholders. Additional Qualifications • Experience operating recommendation, personalization, rank