Principal Software Engineer - AI/ML (Ireland)

Red Hat
4 LocationsPosted 3 April 2026

Job Description

Principal Software Engineer to work on cutting-edge AI/ML applications and agent systems, leveraging modern inference platforms to build production-ready prototypes. You will contribute to upstream communities like vLLM, TGI, PyTorch, and OpenVINO while building innovative applications that demonstrate the capabilities of next-generation AI/ML systems. The ideal candidate is energized by working concurrently on a wide variety of projects, independently as well as within a team environment. What you will do: Build high-quality, high-performing AI/ML applications and agent systems using modern inference platforms for multi-modal and distributed model serving Apply and optimize inference techniques including KV cache management, model quantization, and distributed serving to production workloads Contribute to upstream inference runtime communities such as vLLM, TGI, PyTorch, OpenVINO, and related projects Build multi-modal AI applications integrating vision, language, and other modalities Provide technical leadership and coordination across multiple stakeholders and engineering teams Apply a growth mindset by staying current with rapid advancements in AI/ML inference technologies Benchmark and analyze inference performance at scale, driving data-driven optimization decisions Publicize innovations through blogs, presentations, conferences, and other technical venues What you will bring: Bachelor's degree in Computer Science, Engineering, or equivalent experience 5 years of experience in AI/ML engineering with focus on production inference systems Deep expertise in PyTorch and modern deep learning frameworks Hands-on experience with inference runtime optimization (model serving, batching, KV cache management) Advanced programming skills in Python and C Proven ability to contribute to and lead open source projects Strong self-motivation and organizational skills Ability to work concurrently on multiple projects, independently and within a team environment Excellent English written and verbal communication skills Collaborative attitude and willingness to share ideas openly The following are considered a plus: Experience with vLLM, TGI (Text Generation Inference), or similar inference runtimes Contributions to PyTorch, OpenVINO, or other inference frameworks Experience with distributed model serving and GPU optimization Familiarity with Kubernetes and cloud-native AI/ML deployments Knowledge of model quantization techniques (GPTQ, AWQ, FP8, etc.) Experience with CUDA, Triton, or other GPU programming frameworks Experience with diffusion models and diffusion transformers Experience building AI agents and agentic systems About Red Hat Red Hat is the world’s leading provider of enterprise open source software solutions, using a community-powered approach to deliver high-performing Linux, cloud, container, and Kubernetes technologies. Spread across 40 countries, our associates work flexibly across work environments, from in-office, to office-flex, to fully remote, depending on the requirements of their role. Red Hatters are encouraged to bring their best ideas, no matter their title or tenure. We're a leader in open source because of our open and inclusive environment. We hire creative, passionate people ready to contribute their ideas, help solve complex problems, and make an impact. Inclusion at Red Hat Red Hat’s culture is built on the open source principles of transparency, collaboration, and inclusion, where the best ideas can come from anywhere and anyone. When this is realized, it empowers people from different backgrounds, perspectives, and experiences to come together to share ideas, challenge the status quo, and drive innovation. Our aspiration is that everyone experiences this culture with equal opportunity and access, and that all voices are not only heard but also celebrated. We hope you will join our celebration, and we welcome and encourage applicants from all the beautiful dimensions that c ... (truncated, view full listing at source)
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