Senior Machine Learning Operations Engineer
$139.2k - $219.24kParamount
#WeAreParamount on a mission to unleash the power of content... you in?
We've got the brands, we've got the stars, we've got thepowerto achieve our mission to entertain the planet - now all we're missing is... YOU! Becoming a part of Paramount means joining a team of passionate people who not only recognize the power of content but also enjoy a touch of fun and uniqueness. Together, we co-create moments that matter - both for our audiences and our employees - and aim to leave a positive mark on culture. 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
The 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.
What We Offer:
We've got the brands, we've got the stars, we've got thepowerto achieve our mission to entertain the planet - now all we're missing is... YOU! Becoming a part of Paramount means joining a team of passionate people who not only recognize the power of content but also enjoy a touch of fun and uniqueness. Together, we co-create moments that matter - both for our audiences and our employees - and aim to leave a positive mark on culture. 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: what data trained it, what code produced it, what validation it passed, and how it's performing. 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, don't rely solely on upstream teams to catch their own issues.
- 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, surface feature freshness problems before they cascade.
- Build diagnostic tooling: When something goes wrong, get from "recommendations look off" to root cause in minutes. That means ensuring the right context is logged at each stage, candidates, features, serving context, and building the dashboards to tie it collectively.
- Own incident response for ML systems: Maintain rollback playbooks and pre-defined hotfix strategies with quantified tradeoffs. Own automated 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.
- 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
- Has built or operated model registries, ML monitoring systems, or production ML pipelines
- Understands 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 needing to own the infra yourself
- Experience operating recommendation or personalization systems at scale
The 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.
What We Offer:
- Attractive compensation and comprehensive benefits packages. Check out our full list of benefits here:
- Generous paid time off.
- An exciting and fulfilling opportunity to be part of one of Paramount's most dynamic teams.
- Opportunities for both on-site and virtual engagement events.
- Unique opportunities to make meaningful connections and build a vibrant community, both inside and outside the workplace.
- Explore life at Paramount:
Vacancy posted 6 hours ago
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