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Senior Machine Learning Ops Engineer Jobs in Pennsylvania

Apple

Machine Learning Engineer

Pittsburgh, PA · On-site

As a Machine Learning Engineer in the Machine Intelligence Neural Design (MIND) team, you'll have the opportunity to be part of a leading ML innovation organization that develops a wide range of ...

Apple, Inc.

Machine Learning Engineer

Pittsburgh, PA · On-site

As a Machine Learning Engineer in the Machine Intelligence Neural Design (MIND) team, you'll have the opportunity to be part of a leading ML innovation organization that develops a wide range of ...

New

Apple

Machine Learning Engineer

Pittsburgh, PA

As a Machine Learning Engineer in the Machine Intelligence Neural Design (MIND) team, you'll have the opportunity to be part of a leading ML innovation organization that develops a wide range of ...

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Senior Machine Learning Ops Engineer information

What are the key skills and qualifications needed to thrive as a senior machine learning ops engineer?

To thrive as a Senior Machine Learning Ops Engineer, you need expertise in machine learning, software engineering, cloud platforms, and experience with CI/CD pipelines, often supported by a computer science degree or equivalent experience. Proficiency with tools like Docker, Kubernetes, TensorFlow, PyTorch, and cloud services such as AWS, GCP, or Azure is typically required, along with familiarity with MLOps frameworks. Strong problem-solving, collaboration, and communication skills help you work effectively with cross-functional teams and manage complex ML model deployments. These skills are essential to ensure reliable, scalable, and efficient deployment of machine learning models in production environments.

What are some common challenges faced by senior machine learning ops engineers when deploying models to production?

Senior Machine Learning Ops Engineers often encounter challenges such as ensuring model reproducibility, managing model versioning, and automating deployment pipelines for scalability. Another key challenge is monitoring model performance and data drift in production, which requires robust logging and alerting systems. Collaborating closely with data scientists, software engineers, and IT teams is essential to address these challenges and maintain a stable, efficient ML infrastructure.

What is the difference between Senior Machine Learning Ops Engineer vs Data Engineer?

AspectSenior Machine Learning Ops EngineerData Engineer
CredentialsExperience with ML frameworks, cloud platforms, scripting, and DevOps toolsStrong SQL, ETL, database, and programming skills, often with cloud experience
Work EnvironmentFocus on deploying, monitoring, and maintaining ML models in productionDesigning and building data pipelines and infrastructure for data processing
Industry UsageCommon in AI/ML-focused companies, tech firms, and data-driven organizationsWidespread across industries for data management and analytics

While both roles involve working with data and cloud platforms, the Senior Machine Learning Ops Engineer specializes in deploying and maintaining machine learning models, whereas the Data Engineer focuses on building data pipelines and infrastructure. Understanding these distinctions helps in choosing the right career path or job search focus.

What is a senior machine learning ops engineer?

Senior Machine Learning Ops (MLOps) Engineers are experienced professionals who design, build, and maintain the infrastructure and tools needed to deploy, monitor, and scale machine learning models in production environments. They work at the intersection of data science, software engineering, and DevOps to ensure ML models are robust, reliable, and secure. Their responsibilities often include automating model training pipelines, managing cloud resources, implementing CI/CD for ML, and ensuring model reproducibility. Senior MLOps Engineers also mentor junior staff and help define best practices for the organization’s ML workflow.

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Cities in Pennsylvania with the most Senior Machine Learning Ops Engineer job openings:

Senior Machine Learning Ops Engineer jobs near you

Consultant I (Contractor)

Motion Recruitment Partners, LLC

Philadelphia, PA • On-site

Other

Posted 2 days ago

New

Job description

Empower our team as a Senior Machine Learning Engineer, where you'll build and deploy reliable machine learning models that drive real results. We're seeking someone with deep hands-on experience in traditional ML techniques and Python, your work will be central to our success.
This role is focused on practical model development, not just AI buzzwords. Join us to collaborate with a small, skilled team, sharpen your skills with large-scale data, and contribute directly to production solutions.
Required Skills & Experience
  • Senior-level, hands-on experience as a Machine Learning Engineer (not AI/LLM-focused)
  • Advanced proficiency in Python, with coding skills comparable to a senior software engineer
  • Proven expertise building, training, evaluating, and deploying traditional ML models
  • Practical knowledge of Random Forest, XGBoost, and CatBoost (strongly preferred and currently in use)
  • Experience using PySpark or another distributed processing framework with machine learning workflows
  • Ability to clearly explain ML models and workflow, with real-world examples
  • Experience with data preparation, feature engineering, and model evaluation
Desired Skills & Experience
  • CatBoost production experience is a plus
  • Industry experience is open, telecom not required
  • Strong teamwork and communication skills
  • Previous experience with small, collaborative ML teams
What You Will Be Doing
Tech Breakdown
  • 60% Machine Learning Model Development and Deployment (CatBoost, XGBoost, Random Forest)
  • 20% Big Data Processing and Integration (PySpark or equivalent)
  • 10% Data Preparation, Feature Engineering, Model Evaluation
  • 10% Collaborative Problem-Solving, Documentation, and Team Knowledge Sharing
Daily Responsibilities
  • 70% Hands-On ML Model Building, Training, and Tuning
  • 15% Collaboration & Explaining Models/Results to Peers
  • 10% Technical Documentation & Data Workflows
  • 5% Support & Optimization of Models in Production

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