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Mlops Manager Jobs (NOW HIRING)

Atomic Machines

MLOps Engineer

Emeryville, CA · On-site

We are seeking an MLOps Engineer to join our AI and Modeling & Simulation org within the Data ... management. * Experience with S3, lakehouse technologies such as Apache Iceberg, and workflow ...

Tiger Analytics Inc.

MLOps Lead Engineer

Saint Louis, MO · On-site

$99K - $131K/yr

We are seeking an experienced MLOps Lead Engineer to take full ownership of our enterprise Machine ... Stakeholder Management: Proven track record in a client-facing technical lead role, with strong ...

Tiger Analytics Inc.

MLOps Lead Engineer

Saint Louis, MO · On-site

$99K - $131K/yr

We are seeking an experienced MLOps Lead Engineer to take full ownership of our enterprise Machine ... Stakeholder Management: Proven track record in a client-facing technical lead role, with strong ...

PNC

Quality Engineer Principal - MLOps

Pittsburgh, PA

Key Responsibilities: · Design MLOps testing strategies which verify that the rules, policies, and ... Managing Risk - Assessing and effectively managing all of the risks associated with their business ...

4i Americas

MLOPs Engineer

Concord, CA · On-site

Design, build, and manage end-to-end ML pipelines * Deploy and maintain machine learning models in ... MLOps, DevOps, or ML Engineering * Strong proficiency in Python * Hands-on experience with ML ...

smart folks inc

MLOps Developer

Sunnyvale, CA · On-site

MLOPS Ray Developer Location: Austin, TX/ Sunnyvale, CA/ Remote Duration: Long-term * Deep ... Interact with and support partner teams, including Engineering, QA, and program management.

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Mlops Manager information

What is an MLOps manager?

MLOps Managers are professionals responsible for overseeing the deployment, operation, and scaling of machine learning models in production environments. They coordinate teams to ensure seamless collaboration between data scientists, engineers, and IT staff, facilitating the automation of machine learning workflows. Their role involves managing infrastructure, optimizing processes for model monitoring and maintenance, and ensuring compliance with organizational and industry standards. MLOps Managers play a key role in bridging the gap between model development and operationalization, ensuring that machine learning solutions are reliable, reproducible, and scalable.

What are the key skills and qualifications needed to thrive as an MLOps manager?

To thrive as an MLOps Manager, you need expertise in machine learning, software engineering, and DevOps practices, often backed by a degree in computer science or a related field. Familiarity with tools like Docker, Kubernetes, CI/CD pipelines, cloud platforms (AWS, Azure, GCP), and certifications such as AWS Certified Machine Learning or Google Cloud Professional ML Engineer are highly beneficial. Strong leadership, problem-solving, and cross-functional communication skills help manage teams and bridge the gap between data science and IT operations. These abilities are crucial for ensuring reliable, scalable, and efficient deployment of machine learning solutions in production environments.

What are some common challenges an MLOps manager faces when integrating machine learning models into production environments?

MLOps Managers often encounter challenges such as ensuring seamless collaboration between data science and engineering teams, managing model versioning, and maintaining reliable deployment pipelines. Balancing rapid experimentation with the need for robust, scalable, and secure production systems can be complex. Additionally, monitoring model performance post-deployment and handling data drift or model degradation are ongoing responsibilities. Effective communication and establishing standardized processes are key to overcoming these challenges and ensuring successful model operations.

What is the difference between Mlops Manager vs Data Scientist?

AspectMlops ManagerData Scientist
Required CredentialsBachelor's/Master's in CS, Engineering, or related; certifications in cloud platforms or MLOps toolsBachelor's/Master's in CS, Statistics, or related; certifications in data analysis or machine learning
Work EnvironmentCollaborates with engineering, DevOps, and data teams to deploy and maintain ML systemsAnalyzes data, builds models, and provides insights to inform business decisions
Employer & Industry UsageTech companies, AI startups, enterprises implementing ML pipelinesResearch institutions, tech firms, finance, healthcare, and marketing sectors

The Mlops Manager focuses on deploying, maintaining, and optimizing machine learning systems within an organization, working closely with engineering and DevOps teams. In contrast, a Data Scientist primarily analyzes data, develops models, and provides insights. While both roles require knowledge of machine learning, the Mlops Manager emphasizes operationalizing ML solutions, whereas the Data Scientist emphasizes data analysis and modeling.

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Mlops Manager jobs near you
Infographic showing various Mlops Manager job openings in the United States as of August 2026, with employment types broken down into 87% Full Time, 12% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

MLOps Engineer

Atomic Machines

Emeryville, CA • On-site

Full-time

Posted 5 days ago

Job description

Atomic Machines is ushering in a new era of micromanufacturing with its Matter Compiler technology platform. This platform enables new classes of micromachines to be designed and built by providing manufacturing processes and a materials library that are inaccessible to semiconductor manufacturing methods. It unlocks MEMS manufacturing not only for device classes that could never be produced by semiconductor methods, but also for entirely new categories. Furthermore, this digital platform is fully programmable in the way 3D printing is digital-but whereas 3D printing produces parts of a single material using a single process, the Matter Compiler technology platform is a multi-process, multi-material system: bits and raw materials go in, and complete, functional micromachines come out. The Atomic Machines team has also created an exciting first device-made possible only through the Matter Compiler technology platform-that we will be unveiling to the world soon.
 
Our offices are in Emeryville and Santa Clara, California.
About The Role:

We are seeking an MLOps Engineer to join our AI and Modeling & Simulation org within the Data Engineering and Analytics team.

You will build and operate the infrastructure that takes AI and machine learning models from experimentation to reliable production - covering training, deployment, serving, monitoring, and continuous improvement. This is a DevOps-leaning MLOps role centered on the model feedback loop: connecting production signals and expert feedback back to training so models improve as the system operates.

We are looking for senior-level candidates who can take meaningful ownership of production ML infrastructure. The scope and seniority of the role will be shaped by the candidate's experience, technical depth, and demonstrated impact.

You will work closely with Data, AI, Process, Design, and Software engineers in a highly cross-functional environment.

What You'll Do:
  • Build and evolve the MLOps platform and CI/CD: Own the path from experiment to production, including experiment tracking, model registry, packaging, automated training and retraining, deployment, and safe rollout and rollback.
  • Operate model serving infrastructure: Build reliable, scalable batch, streaming, and real-time inference for models and digital twins supporting design, process control, scheduling, and inspection.
  • Build ML data and feature pipelines: Turn machine telemetry, process and knowledge graphs, images, time-series, agentic conversations, and other production data into contextualized, model-ready datasets and features.
  • Maintain ML data infrastructure: Support feature-store capabilities and a lakehouse foundation using Apache Iceberg on S3, with strong data quality, lineage, versioning, and reproducibility.
  • Close the model feedback loop: Build model observability and human-in-the-loop systems that capture production signals and expert corrections, version them as ground truth, and feed them into evaluation and retraining workflows.
  • Create paved roads for ML development: Develop standardized tooling and workflows that enable Data and AI engineers to move quickly while maintaining production reliability and reproducibility.
  • Drive technical ownership: Identify infrastructure, reliability, and scalability challenges and drive solutions from design through production. More senior candidates will have opportunities to shape architecture, technical direction, and engineering practices across the ML platform.
  • Collaborate across disciplines: Work with Process, Chemical, Materials, Simulation, Software, Data, and AI engineers to define deployment, serving, and data-collection requirements.
What You'll Need:
  • 5+ years of relevant industry experience building production software, infrastructure, data, or machine learning systems. We value demonstrated technical depth, ownership, and impact over a specific number of years.
  • Proven experience building and operating machine learning systems in production, with a strong MLOps/DevOps orientation.
  • Strong DevOps fundamentals, including CI/CD, containers, Kubernetes, cloud infrastructure, and infrastructure-as-code.
  • Proficiency in Python and SQL.
  • Hands-on experience with MLflow or similar tooling for experiment tracking, model registry, and model lifecycle management.
  • Experience with S3, lakehouse technologies such as Apache Iceberg, and workflow orchestration tools such as Airflow or Dagster.
  • Experience building pipelines for multimodal ML data, including images, time-series, structured, and semi-structured data.
  • Familiarity with manufacturing systems, sensors, process automation, or other physical-world data systems.
  • Strong problem-solving skills, attention to data quality and reliability, and clear technical communication.
  • Bachelor's or Master's degree in Computer Science, Data Engineering, Data Science, or a related STEM field, or equivalent practical experience.
  • This role is open across multiple levels, from early in career though Staff (L4 through L6). We'll determine the appropriate level and compensation based on your experience, skills, and the scope of the role through the interview process.
Bonus Points For:
  • Experience with feature stores, human-in-the-loop systems, active learning, or data-labeling infrastructure.
  • Robotics or robotic automation experience, including sensors, vision systems, or robotics data.
  • Experience operating ML systems in manufacturing or other physical-world environments.
  • Experience building internal tools for expert feedback, labeling, model evaluation, or model interaction.
  • Experience designing shared ML infrastructure or platforms used across multiple teams or applications.

Apply