{"id":4243785,"url":"https://alion.io/job/paramount-lead-machine-learning-operations-engineer","title":"Lead Machine Learning Operations Engineer","company":{"id":6984,"name":"Paramount","domain":"paramount.com","url":"https://alion.io/company/paramount","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SuccessFactors","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Burbank, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":156800,"max":235200,"currency":"USD","period":"year","gross":null,"usd_annual":235200},"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A/B Testing","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"Feature Store","optional":false},{"name":"Machine Learning","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Progressive Delivery","optional":false},{"name":"Recommender Systems","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-10-27T07:00:00Z","employer_posted_date":"2026-10-27","last_verified_at":"2026-11-13T12:27:52Z","board_verified":true,"closed_at":null,"days_open":17,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":17},"description":"Storytelling is, and will always be, the heart of what we do at Skydance.\nOur 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.\nHere, 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.\nIf 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!\nLead Machine Learning Operations Engineer\nPersonalization & Recommendation Systems\nOverview\nWe’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.\nOur 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.\nThis is a lead-level IC role: you’ll set technical direction and drive adoption across ML Engineering, DevOps, Platform Engineering, Data Engineering, and Product.\nSitting 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.\nWhat You’ll Do\nOwn ML production reliability strategy\nDefine and lead the operational strategy for production ML systems, including monitoring, traceability, deployment safety, incident response, and post-deployment validation.\n\nSet the standards ML teams use to assess model health, performance, and trustworthiness in production.\n\nOwn model traceability and governance\nEnsure 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.\n\nBuild end-to-end ML observability\nDesign 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.\n\nDefine production health metrics\nPartner with ML, data, product, and business stakeholders to define post-deployment metrics covering model quality, system reliability, business guardrails, and degradation indicators.\n\nDetect drift and degradation proactively\nDetect data drift, feature drift, model behavior changes, and silent failures before they impact customers via thresholding, alerting, anomaly detection, and release-over-release monitoring.\n\nLead diagnostic tooling and root-cause analysis\nBuild 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.\n\nOwn ML deployment safety\nDefine 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.\n\nLead ML incident response\nOwn 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.\n\nPartner across ML Platform, Data, and ML\nPartner 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.\n\nSet standards and mentor others\nAct as the technical lead for ML operations: establish reusable patterns, playbooks, and standards, and mentor engineers on reliability, observability, and operational rigor.\n\nBasic Qualifications\n5+ years of experience in machine learning engineering, ML platform, applied ML, MLOps, data platform, reliability engineering, or a related technical role.\n\nDemonstrated experience operating production ML systems, including monitoring, deployment, incident response, model validation, data quality, or reliability ownership.\n\nExperience leading technical initiatives across multiple engineering teams, especially where success required influencing architecture, tooling, standards, or adoption.\n\nHands-on experience with model registries, feature stores, ML metadata systems, production monitoring, model deployment pipelines, or ML observability platforms.\n\nSolid knowledge of end-to-end ML systems, including training data, features, model artifacts, offline validation, online serving, post-deployment metrics, and business outcome measurement.\n\nAbility to reason about ML operational failure modes: stale features, distribution shift, training-serving skew, delayed labels, and offline-online metric gaps.\n\nSolid SQL skills and comfort investigating data quality, feature distributions, model outputs, pipeline behavior, and production anomalies.\n\nTrack record of cross-functional collaboration with Platform, Data, and ML Engineering to deliver production-grade operational capabilities.\n\nSolid written and verbal communication skills, including the ability to explain ML system health, risks, incidents, and tradeoffs to both technical and non-technical stakeholders.\n\nAdditional Qualifications\nExperience operating recommendation, personalization, ranking, search, ads, content discovery, or marketplace ML systems at scale.\n\nExperience with real-time or near-real-time model serving systems.\n\nExperience with feature stores, model registries, metadata stores, experiment tracking, data quality tools, lineage systems, or observability platforms.\n\nExperience designing automated validation gates, canary deployments, rollback strategies, shadow deployments, or progressive delivery workflows for ML systems.\n\nExperience with A/B testing, experiment guardrails, counterfactual evaluation, or offline-to-online metric alignment.\n\nExperience with cloud-native production environments, distributed data pipelines, orchestration frameworks, or streaming systems.\n\nExperience leading incident reviews, post-mortems, reliability programs, or operational excellence initiatives.\n\nExperience defining standards, playbooks, or governance frameworks for production ML systems.\n\nWhat Success Looks Like\nWithin the first 90 days, you will have assessed the current production ML operational landscape, identified the highest-risk gaps, and established a prioritized roadmap for observability, traceability, deployment safety, and incident response.\n\nWithin six months, our production recommendation systems will have clearer model lineage, stronger monitoring coverage, better diagnostic workflows, and more reliable deployment gates.\n\nWithin one year, ML operations will be a repeatable, trusted function: teams will know what is running, why it was promoted, how it is performing, when something is wrong, and how to recover quickly.\n\nLeveling Signal\nThe right candidate defines strategy, leads cross-functional execution, and sets engineering standards while staying hands-on enough to investigate production issues directly.\nParamount Streaming, a division within Paramount Global, is the home to the company's direct-to-consumer services spanning free and paid in the form of Pluto TV and Paramount+. Pluto TV is the global leader in free ad-supported TV, delivering more than 1,400 global channels and an extensive library of streaming content, including live and original channels. Paramount+, digital subscription video-on-demand and live streaming service, combines live sports, breaking news, and A Mountain of Entertainment™. Paramount+ features an expansive library of original series, hit shows and popular movies across every genre from world-renowned brands and production studios, including SHOWTIME®.\nADDITIONAL INFORMATION\nHiring Salary Range: $156,800.00 - 235,200.00.\nThe hiring salary range for this position applies to New York, California, Colorado, Washington state, and most other geographies. Starting pay for the successful applicant depends on a variety of job-related factors, including but not limited to geographic location, market demands, experience, training, and education. The benefits available for this position include medical, dental, vision, 401(k) plan, life insurance coverage, disability benefits, tuition assistance program and PTO or, if applicable, as otherwise dictated by the appropriate Collective Bargaining Agreement. This position is bonus eligible.\nWhat We Offer:\nAttractive compensation and comprehensive benefits packages. Check out our full list of benefits here.\nGenerous paid time off.\nComplimentary subscriptions to HBO Max, Paramount+, and other Skydance streaming services and digital news products.\nRobust employee engagement opportunities, including access to exclusive screenings and events, speaker series, volunteer activities and more.\nSkydance is an equal opportunity employer (EOE)\nWe embrace the opportunity to build a workforce that reflects a wide array of perspectives, backgrounds and experiences. Being an equal opportunity employer means that we take seriously our responsibility to consider qualified candidates on the basis of merit. In line with the laws of the country or region in which the role is based, this includes consideration without regard to race, color, ethnicity, national origin, citizenship status, religion or belief, sex, gender, gender identity or expression, sexual orientation, age, marital status, pregnancy, parenthood, physical or mental disability, genetic information, military or protected veteran status, union status or any other category protected by applicable law.\nIf you are a qualified individual with a disability, you may request a reasonable accommodation during the job application and/or recruitment process. You can request reasonable accommodations by calling 212.846.5500 or by sending an email to . 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