Senior Machine Learning Engineer

At Red Hat, we connect an innovative community of customers, partners, and contributors to deliver an open source stack of trusted, high-performing solutions. We offer cloud, Linux, middleware, virtualization, and AI technologies, together with award-winning global customer support, consulting, and implementation services. Red Hat is a rapidly-growing company supporting more than 90% of Fortune 500 companies.

About the Job

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 Senior Machine Learning Engineer to join the Agentic and AI Engineering Tools team. 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 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.

What you will do

• Build developer-facing capabilities to enable agentic reasoning using large language models.

• Establish best-practices for using those capabilities effectively.

• Enable developers to evaluate how effective and reliable the agentic reasoning system they create are.

• Provide scalable, secure, and robust infrastructure for tools used in agentic reasoning.

• 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.

What you will bring

• 5+ years of experience as a Machine Learning Engineer, Software Engineer, Data Scientist, or similar role.

• Bachelor's degree in Computer Science or related discipline.

• Machine learning, AI, or deep learning-related course work or experience or independent project work with evidence of completion.

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

• Experience writing unit, functional, and end-to-end tests for predictive or generative AI applications.

• Experience preparing structured datasets for training and analysis.

• Experience with feature engineering to enhance the utility of ML models.

• Experience with model performance evaluation using predefined metrics to assess model accuracy and identify improvements.

• Experience deploying machine learning models into test environments.

Following is considered a plus

• Experience building agentic systems with MCP, ACP, or A2A.

• 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.

• Participating in an agile development team.