Senior Machine Learning Operations Engineer, ModelOps and Runtime Platform EngineeringParamount Pictures • United States
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Senior Machine Learning Operations Engineer, ModelOps and Runtime Platform Engineering
Paramount Pictures
- United States
- United States
À propos
Overview We're hiring a Senior Machine Learning Operations Engineer to own the operational layer around our personalization and recommendation Machine Learning (ML) systems. Our models retrain and deploy daily on automated pipelines. Your job is to make sure we can trust what's running, know when something is off, and fix it fast.
You’ll sit within DevOps and work closely with ML engineers, who own the models end‑to‑end. You won’t be building infrastructure from scratch; you’ll partner with DevOps and Platform Engineering to get the tooling you need, then own it day‑to‑day.
What You'll Do
Own model traceability: every model in production should have clear lineage, including training data, code, validation, and performance. Evaluate and recommend tooling for versioning, metadata, and model registry, and work with MLEs to drive adoption.
Build end‑to‑end monitoring: monitor the full signal path—data arrival, feature distribution stability, model metrics, and serving latency against SLA. Own this individually, rather than relying solely on upstream teams.
Partner with Data Engineering on data quality: collaborate to surface data quality issues, detect drift in upstream sources, and ensure features stay fresh and reliable.
Detect issues proactively: track drift over weeks, flag slow degradation before it crosses a threshold, and surface feature freshness problems before they cascade.
Build diagnostic tooling: ensure the right context is logged at each stage (candidates, features, serving context) and build dashboards to tie it collectively to resolve issues quickly.
Own incident response for ML systems: maintain rollback playbooks, pre‑defined hotfix strategies with quantified tradeoffs, automate gates that block bad deployments, run post‑mortems, and close the gaps.
Coordinate on post‑deployment metrics: work with ML engineers, data engineers, and stakeholders to define what metrics to collect after deployment and why they matter.
Basic Qualifications
5+ years in ML engineering, applied ML, or a related ML role with demonstrated experience on the operational side of monitoring, reliability, deployment, or incident response.
Experience building or operating model registries, ML monitoring systems, or production ML pipelines.
Understanding of ML systems end‑to‑end – not just the infra layer, but why a stale feature or a shifted distribution matters.
Robust SQL skills and comfort digging into data distributions, feature health, and model behavior.
Comfortable partnering with DevOps and Platform teams to define infrastructure needs without owning the infra yourself.
Additional Qualifications
Experience operating recommendation or personalization systems at scale.
Salary and Benefits Hiring salary range: $139,000 – $175,000. Benefits include medical, dental, vision, 401(k), life insurance, disability, tuition assistance, PTO, and bonus eligibility.
What We Offer
Attractive compensation and comprehensive benefits packages. Full list available at https://www.paramount.com/careers/benefits
Generous paid time off.
Vibrant team with on‑site and virtual engagement events.
Opportunities to build community inside and outside the workplace.
Explore life at Paramount at https://www.paramount.com/careers/life-at-paramount
Paramount is an equal opportunity employer (EOE) including disability/vet. For qualifications and benefits, see full policies at https://www.paramount.com/careers.
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Compétences linguistiques
- English
Avis aux utilisateurs
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