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Learning Operations Manager Jobs (NOW HIRING)

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Key Responsibilities Learning Operations & LMS Support * Manage and update learning content within the Learning Management System (LMS) * Create and maintain course schedules, rosters, completions ...

Summary The Learning Operations Admin is a temporary hourly role that helps keep Curana's Learning ... This person will help triage and manage the learning inbox and company intranet inbox, maintain ...

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Learning Operations Manager information

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$31K

$63.5K

$118.5K

How much do learning operations manager jobs pay per year?

As of Jun 19, 2026, the average yearly pay for learning operations manager in the United States is $63,456.00, according to ZipRecruiter salary data. Most workers in this role earn between $41,000.00 and $77,500.00 per year, depending on experience, location, and employer.

How does a Learning Operations Manager typically collaborate with instructional designers and trainers within an organization?

A Learning Operations Manager works closely with instructional designers to ensure that course development aligns with organizational goals, timelines, and quality standards. They coordinate with trainers to schedule sessions, manage resources, and gather feedback for continuous improvement. Regular meetings and open communication channels are essential to address logistical challenges, troubleshoot issues, and ensure a seamless learning experience for participants. This collaborative approach helps streamline training delivery and promotes a culture of ongoing learning within the organization.

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

To thrive as a Learning Operations Manager, you need expertise in program management, data analysis, instructional design, and often a background in education or business. Familiarity with learning management systems (LMS), project management tools, and data reporting platforms is typically required. Strong organizational skills, problem-solving abilities, and effective communication set top performers apart in this role. These competencies ensure smooth delivery of training programs, data-driven improvements, and alignment with organizational learning goals.

What is a Learning Operations Manager?

A Learning Operations Manager is responsible for overseeing the planning, execution, and optimization of training programs within an organization. They coordinate logistics, manage learning technologies, and ensure that educational initiatives run smoothly and efficiently. This role often works closely with instructional designers, trainers, and other stakeholders to align learning activities with organizational goals. Their work helps to maximize the impact and effectiveness of professional development and training efforts.

What is the difference between Learning Operations Manager vs Learning Coordinator?

AspectLearning Operations ManagerLearning Coordinator
CredentialsTypically requires a bachelor’s degree in education, business, or related field; certifications in learning management systems (LMS) are commonUsually requires a bachelor’s degree; certifications in training or LMS are beneficial
Work EnvironmentOversees learning programs, manages teams, and collaborates with stakeholders in corporate or educational settingsSupports training sessions, coordinates schedules, and assists in content delivery within organizations
Employer & Industry UsageUsed in corporate training, e-learning companies, and educational institutionsCommon in corporate training departments, nonprofits, and educational organizations

The Learning Operations Manager focuses on managing learning programs, teams, and operational processes, while the Learning Coordinator handles logistical support and coordination of training activities. Both roles require knowledge of learning systems, but the manager has broader responsibilities in strategy and oversight.

More about Learning Operations Manager jobs
What cities are hiring for Learning Operations Manager jobs? Cities with the most Learning Operations Manager job openings:
What states have the most Learning Operations Manager jobs? States with the most job openings for Learning Operations Manager jobs include:
Infographic showing various Learning Operations Manager job openings in the United States as of June 2026, with employment types broken down into 1% As Needed, 84% Full Time, 13% Part Time, 1% Temporary, and 1% Contract. Highlights an 92% Physical, 2% Hybrid, and 6% Remote job distribution, with an average salary of $63,456 per year, or $30.5 per hour.
Manager, Machine Learning Operations (MLOps)

Manager, Machine Learning Operations (MLOps)

Zefr, Inc

Marina Del Rey, CA • On-site

$170K - $230K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

This job post has expired today. Applications are no longer accepted.


Job description

What We Do:
Zefr is the global leader in brand suitability targeting and measurement across the world's largest platforms. Zefr's technology is helping to power the age of responsible marketing by putting advertisers in control of their content adjacencies based on their own unique brand safety and suitability preferences. As an official YouTube Measurement Program Partner, Meta for Business Partner, and TikTok for Business Partner, the company leverages patented machine learning and AI technology (Cognition AI) to offer brands and agencies more precise and transparent brand safety and suitability activation and measurement solutions on scaled platforms. The company is headquartered in Los Angeles, California, with additional locations across the globe.
What You'll Do:
We are hiring a Manager of Machine Learning Operations to lead our ML Ops team and drive the infrastructure, tooling, and processes that enable our machine learning systems to operate at scale. You will oversee the deployment, monitoring, and optimization of ML models that process multi-terabytes of social media platform data from TikTok, YouTube, Facebook, Instagram, and Snap. In this role, you will lead a team of engineers responsible for building and maintaining robust ML pipelines, ensuring model reliability in production, and implementing best practices for model lifecycle management. You will collaborate closely with ML Engineers and Data Scientists to bridge the gap between research and production. We are excited to welcome a leader who is passionate about building scalable ML infrastructure and developing high-performing teams.
Key Responsibilities:
• Lead, mentor, and grow a team of Machine Learning Engineers, fostering a culture of innovation and continuous improvement
• Design and implement scalable ML infrastructure for model training, deployment, and serving
• Establish and enforce best practices for ML model lifecycle management, including versioning, testing, and monitoring
• Develop and maintain CI/CD pipelines for machine learning workflows
• Optimize model inference performance and reduce latency/cost across production systems
• Collaborate with ML Engineers and Data Scientists to productionize models efficiently
• Implement robust monitoring, alerting, and observability solutions for ML systems
• Drive technical decisions on ML Ops tooling, infrastructure, and architecture
• Ensure high availability and reliability of ML services at scale
• Manage project timelines, priorities, and resource allocation for the ML Ops team
Tech Stack:
Languages: Python, SQL
Data Stores: Snowflake, Qdrant, GCS
Data Processing: DBT, Pandas, Ray
DevOps: GitHub Actions, Docker, Terraform, Kubernetes, ArgoCD, AWS, GCP, Datadog
MLOps: Triton Inference Server, Weights and Biases, ONNX, TensorRT LLM, vLLM, SGLang
ML: Voxel51 Teams, Transformers, PyTorch, HuggingFace
What We're Looking For:
• Bachelor's or Master's degree in Computer Science or related field with 5+ years of professional experience in ML Engineering or MLOps
• 1+ years of experience leading or guiding engineering teams in either formal or informal leadership roles
• Deep expertise in ML model deployment, serving infrastructure, and production ML systems
• Hands-on experience with transformer architectures (e.g., BERT, ViT) for natural language and vision tasks.
• Strong understanding of multimodal embedding techniques for integrating text, image, audio, and structured data.
• Experience with LLM models such as Gemini, GPT, Claude, Qwen, etc.
• Experience with ML experiment tracking, model versioning, and feature stores
• Strong understanding of CI/CD principles applied to ML workflows
• Experience optimizing model inference performance (ONNX, TensorRT, or similar)
• Excellent leadership, communication, and stakeholder management skills
• Track record of building and scaling high-performing engineering teams
• Openness to new technologies and creative solutions
Nice to Have:
• Experience with ad tech and digital advertising ecosystem
• Experience with multimodal LLM fine-tuning
Benefits (for US-based employees):
• Flexible PTO
• Medical, dental, and vision insurance with FSA options
• Company-paid life insurance
• Paid parental leave
• 401(k) with company match
• Professional development opportunities
• 14 paid holidays off
• Flexible hybrid work schedule
• "Summer Fridays" (shorter work days on select Fridays during the summertime)
• In-office lunches and lots of free food
• Optional in-person and virtual events (we like to celebrate!)
Compensation (for US-based employees):
The anticipated base salary for this position is between $170,000 and $230,000. Within the range, individual pay is determined by factors such as job-related skills, experience, and relevant education or training. If your compensation expectations fall outside of this range, it may still be worth having a conversation.
Zefr is an equal opportunity employer that embraces diversity and inclusion in the workplace. We are committed to building a team that represents a variety of backgrounds, skills, and perspectives because we know this only makes us better. We strongly encourage women, persons of color, LGBTQIA+ individuals, persons with disabilities, members of ethnic minorities, foreign-born residents, and veterans to apply even if you do not meet 100% of the qualifications.