Principal Machine Learning Engineer (Data Processing)

Job Summary

At Red Hat, our commitment to open source innovation extends beyond our products - it’s embedded in how we work and grow. Red Hatters embrace change – especially in our fast-moving technological landscape – and have a strong growth mindset. That's why we encourage our teams to proactively, thoughtfully, and ethically use AI to simplify their workflows, cut complexity, and boost efficiency. This empowers our associates to focus on higher-impact work, creating smart, more innovative solutions that solve our customers' most pressing challenges.

Red Hat’s Global Engineering team is looking for an experienced Machine Learning Engineer to join the Agentic and AI Engineering Tools team in Data Processing. In this role, you’ll contribute directly to Red Hat’s rapidly growing AI/ML family of products and will be responsible for the investigation, evaluation, integration, and development of open source AI/ML systems and functionality to improve the overall development and operations of both Red Hat’s downstream AI products and upstream open source AI projects. The Data Processing team is responsible for creating the libraries and tooling that will enable Data Scientists, Machine Learning Engineers and AI Engineers to ingest data and process it for model customization or AI application development.

The ideal candidate will have a proven background in delivering enterprise level AI/ML solutions. As part of your responsibilities, you will regularly participate in design reviews, contribute to the productization of major features, and support bug fixes.

Primary Job Responsibilities

• Advanced Data Preprocessing: Prepare structured and unstructured data for analysis, including handling missing values and outliers.

• Assist with Integration: May contribute to building model serving solutions to accommodate a variety of model architectures and accelerators

• Team Collaboration: Work with software engineers, data scientists, and product managers to meet project requirements.

• Mentorship: Provide technical guidance and training to junior engineers

• Design and Train Models: Train ML models using established frameworks and architectures.

• Proactively utilize AI-assisted development tools (e.g., GitHub Copilot, Cursor, Claude Code) for code generation, auto-completion, and intelligent suggestions to accelerate development cycles and enhance code quality.

• Explore and experiment with emerging AI technologies relevant to software development, proactively identifying opportunities to incorporate new AI capabilities into existing workflows and tooling.

Required Skills

• Bachelor's degree in computer science or other related discipline/equivalent years of experience plus 10 years professional experience

• Advanced knowledge of machine learning, AI, or deep learning-related experience or independent project work with evidence of completion.

• Advanced knowledge of Data Ingestion or Processing tools or frameworks, using such Docling, Spark, Pandas, Feast, Airflow, NumPy

• Advanced experience with Jupyter notebooks and Python development.

• Perform Data Preprocessing: Clean and prepare structured datasets for training and analysis.

• Conduct Feature Engineering: Create basic features to enhance data utility for machine learning models.

• Evaluate Model Performance: Use predefined metrics to assess model accuracy and identify improvements.

• Collaborate on Deployment: Contribute to the deployment of machine learning models into test environments.

• Documentation Support: Document processes and maintain records of datasets and models used.

• Experience in understanding and implementing concepts outlined in research papers. Experience with writing and publication of research papers is a plus.

• Write unit tests for ML models and associated components

Nice to Have

• Masters or PhD in Machine Learning (ML) / Natural Language Processing (NLP).

• Experience working with Kubernetes/OpenShift and containers, troubleshooting issues with them, and working with YAML, Kubernetes controllers, and operators.

• Understanding of DevOps methodology, scrum, and/or Jira.

• Familiarity with participating in an agile development team.

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