Principal Software Engineer (Full Stack)

Job Summary

Come be a part of Red Hat's charge to democratize AI with open source! Red Hat's Global Engineering Team is looking for a Principal Software Engineer to join our newly formed AI Engineering organization. This role will be located within the InstructLab and Granite team, and will be focused on building software tooling to support one or more components of a large language model (LLM) customization pipeline - from document ingestion, to synthetic data generation, to model fine tuning, to model evaluation - as well as the mentorship and building of these skills internal to our Red Hat team. 

In this role, you will serve as a bridge between Red Hat and our peer IBM Research team – this will involve participating in the development of novel techniques and research extensions alongside IBM Research. This role also requires engaging with related upstream open source communities and projects. You will develop working relationships across multiple teams, planning and prioritizing sprint work across a small team, along with direct contribution to development projects at a senior level of ability.

The ideal candidate will be a highly collaborative individual with a passion for working on complex projects in an open organization where contributions are valued and expected from all levels. As this is a fast-moving area of opportunity for Red Hat, the ability to communicate productively and effectively with team members, stakeholders, and Red Hat leadership is critical.

This position reports directly to the Manager of Software Engineering for InstructLab. This position may require occasional travel to partner collaboratively in our Boston, MA office multiple times per quarter. Successful applicants must reside in a state where Red Hat is registered to do business.

Primary Job Responsibilities (what you’ll do)

• Design, implement, and optimize AI tooling and systems to improve the quality and relevance of generated content produced by models created by the end-to-end pipeline.

• Develop retrieval mechanisms to efficiently access and leverage external data sources.

• Train and fine-tune generative models with retrieved data to enhance performance and accuracy.

• Evaluate model performance and iterate on improvements based on metrics and user feedback.

• Work closely with data scientists, product managers, and other stakeholders to understand requirements and deliver effective solutions.

• Participate in code reviews and collaborate on best practices within the engineering team.

• Stay up-to-date with the latest advancements in AI, natural language processing (NLP), RAG methodologies, and other related technologies.

• Participate in upstream generative AI model projects such as lm-eval, ragas.io, LlamaIndex, LangChain, Hugging Face Transformers, vllm, pytorch, etc.

• Document system designs, processes, and model performance for transparency and future reference.

• Report on project status, challenges, and results to stakeholders.

• Serve as a subject matter expert for your assigned component, providing mentorship and expertise to build knowledge and capabilities within Red Hat teams. 

• Gather and analyze user feedback to refine and enhance AI tooling.

Required Skills (what you’ll bring)

• Bachelor's degree in computer science or equivalent.

• Advanced programming skills in Python and SQL.

• Experience in or familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch).

• Understanding of natural language processing (NLP) techniques and models.

• Familiarity with retrieval-augmented generation architectures and methodologies.

• Experience with data processing and manipulation libraries.

• Knowledge of microservices and containerization technologies (e.g., Kubernetes) for LLM deployment.

• Strong self-motivation and organizational skills.

• Demonstrated ability to context switch between multiple concurrent projects.

• Outstanding mentorship and coaching skills.

• Excellent written and verbal communication skills.

• Positive attitude and willingness to share ideas openly.

Bonus qualifications

• Masters or PhD in M