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Machine Learning Operations Engineer Jobs (NOW HIRING)

Apple

Machine Learning Engineer

Cupertino, CA · On-site

Bachelor's Degree in Computer Science, Statistics, Data Mining, Machine Learning, Operations Research, or related field.Proficiency in one or more object-oriented programming languages such as Python ...

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Machine Learning Operations Engineer information

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How much do machine learning operations engineer jobs pay per year?

As of Jun 15, 2026, the average yearly pay for machine learning operations engineer in the United States is $85,029.00, according to ZipRecruiter salary data. Most workers in this role earn between $69,500.00 and $94,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Machine Learning Operations Engineer, and why are they important?

To thrive as a Machine Learning Operations Engineer, you need a strong background in computer science, machine learning principles, and software engineering, typically with a bachelor's or master's degree in a related field. Familiarity with cloud platforms (like AWS, GCP, or Azure), containerization tools (such as Docker and Kubernetes), and CI/CD pipelines, as well as experience with MLOps frameworks (like MLflow or Kubeflow), is essential. Excellent problem-solving, collaboration, and communication skills help bridge the gap between data science and IT teams. These skills ensure efficient deployment, monitoring, and scaling of ML models, enabling reliable and maintainable AI solutions in production environments.

What is MLOps' salary?

The salary for a Machine Learning Operations (MLOps) Engineer typically ranges from $90,000 to $150,000 annually, depending on experience, location, and company size. Senior roles or those in high-demand regions can offer higher compensation, often including benefits related to cloud platforms and automation tools.

What engineers make $500,000?

Senior machine learning operations engineers with extensive experience, specialized skills in cloud platforms, automation, and large-scale deployment can reach or exceed $500,000 in total compensation, especially in high-cost-of-living areas or within large tech companies. Achieving this level often requires advanced certifications, leadership roles, and a strong track record in deploying scalable AI systems.

What is a ML operations engineer?

A Machine Learning Operations (MLOps) engineer is responsible for deploying, managing, and maintaining machine learning models in production environments. They work with tools like Docker, Kubernetes, and cloud platforms to ensure models are scalable, reliable, and integrated into applications, often collaborating with data scientists and software engineers.

How does a Machine Learning Operations Engineer typically collaborate with data scientists and software engineers on production projects?

Machine Learning Operations Engineers play a crucial role in bridging the gap between data scientists, who develop models, and software engineers, who deploy applications. They work closely with data scientists to understand the requirements and constraints of ML models, ensuring smooth transition from prototype to production. MLOps Engineers also collaborate with software engineers to integrate models into scalable, reliable systems while managing version control, monitoring, and continuous delivery pipelines. Effective communication and cross-functional teamwork are essential to address challenges like model drift, resource allocation, and deployment automation.

How much do machine learning ops engineers make?

Machine Learning Operations (MLOps) engineers typically earn between $90,000 and $150,000 annually, depending on experience, location, and company size. Senior roles or those with specialized skills in cloud platforms and automation tools can earn higher salaries, often exceeding $160,000 per year.

What is a Machine Learning Operations Engineer?

A Machine Learning Operations (MLOps) Engineer is a professional who specializes in deploying, managing, and maintaining machine learning models in production environments. They bridge the gap between data science and IT operations, ensuring that machine learning solutions are scalable, reliable, and efficient. MLOps Engineers automate workflows, monitor model performance, and address issues related to model versioning, data drift, and system integration. Their work is crucial for enabling organizations to leverage AI at scale while maintaining compliance and reliability.
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What job categories do people searching Machine Learning Operations Engineer jobs look for? The top searched job categories for Machine Learning Operations Engineer jobs are:
Machine Learning Operations Engineer jobs near you
Infographic showing various Machine Learning Operations Engineer job openings in the United States as of June 2026, with employment types broken down into 77% Full Time, 11% Part Time, 3% Temporary, 6% Contract, and 3% Nights. Highlights an 87% Physical, 5% Hybrid, and 8% Remote job distribution, with an average salary of $85,029 per year, or $40.9 per hour.
Senior Machine Learning Operations Engineer

Senior Machine Learning Operations Engineer

Paramount

Manhattan, NY • On-site

$146K - $219K/yr

Other

Medical, Dental, Vision, Life, Retirement, PTO

Posted 14 days ago

Paramount Senior Living rating

5.3

Company rating: 5.3 out of 10

Based on 19 frontline employees who took The Breakroom Quiz

Job description

#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 the power to 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.

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
  • 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

Additional Qualifications
  • Experience operating recommendation or personalization systems at scale

Paramount 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.

ADDITIONAL INFORMATION

Hiring Salary Range: $146,160.00 - 219,240.00. 

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: https://www.paramount.com/careers/benefits
  • 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: https://www.paramount.com/careers/life-at-paramount

Paramount is an equal opportunity employer (EOE) including disability/vet.

At Paramount, the spirit of inclusion feeds into everything that we do, on-screen and off. From the programming and movies we create to employee benefits/programs and social impact outreach initiatives, we believe that opportunity, access, resources and rewards should be available to and for the benefit of all. Paramount is proud to be an equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ethnicity, ancestry, religion, creed, sex, national origin, sexual orientation, age, citizenship status, marital status, disability, gender identity, gender expression, and Veteran status.

If you are a qualified individual with a disability or a disabled veteran, you may request a reasonable accommodation if you are unable or limited in your ability to use or access https://www.paramount.com/careers as a result of your disability. You can request reasonable accommodations by calling 212.846.5500 or by sending an email to paramountaccommodations@paramount.com. Only messages left for this purpose will be returned.

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