Huawei Ireland Research Centre
2026 - Senior/Staff Machine Learning Engineer - Multimodal Content Intelligence - Permanent
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Location: Dublin, Ireland
About Huawei
Huawei is a leading global provider of information and communications technology (ICT) infrastructure and smart devices. We are committed to bringing digital to every person, home and organization for a fully connected, intelligent world.
At Huawei, we have two key drivers of innovation: science and technology, and customer needs. Both commercial value and market demands are driving our innovation and determining how we invest in science and technology. Breakthroughs in technology, in return, stimulate customer needs and allow us to create greater value for customers.
About the IRC
Huawei Ireland Research Centre's (IRC) mission is to position Huawei as a recognised technology leader and global information and communications technology (ICT) solutions provider. We are building an industry-recognised multi-disciplinary Research Centre of experts focusing on medium-term to long-term issues to achieve this. The IRC will work closely with an open, innovative ecosystem with Huawei customers to address real-world issues. The IRC will also engage with key European universities to build a basic research capability to support Huawei technical projects.
About the Job
The Terminal Cloud Technology Lab is looking for a Senior/Staff Machine Learning Engineer to drive the development of our next-generation content intelligence systems, with a strong focus on video understanding and Vision-Language Models (VLMs). In this role, you will be both a hands-on technical expert and a technical leader, advancing the SOTA in multimodal content understanding while guiding a small group of engineers through complex technical problems. Your work will be instrumental in enhancing our core recommendation engine, directly impacting product performance and user engagement through advanced content signals.
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Responsibilities
- Drive Video & Multimodal Understanding: Lead the development of video understanding and implementation of multimodal frameworks for semantic understanding, narrative reasoning, and high-level content interpretation.
- Build Structured Content Intelligence: Design and own tagging taxonomies, classification systems, and knowledge graph structures that turn raw multimodal content into structured, actionable signals.
- Provide Technical Leadership: Serve as the technical point of contact for a small group within the team, unblocking colleagues on complex technical challenges and ensuring high-quality execution on shared initiatives.
- Adopt New Technology: Stay ahead of state-of-the-art research in multimodal learning and VLMs, independently evaluating, prototyping, and bringing promising techniques from research to production.
- Own Technical Execution: Drive end-to-end development of high-performance systems, resolving high-impact bottlenecks in accuracy, latency, and scalability.
- Collaboration & Integration: Work closely with cross-functional teams to integrate content signals into production recommendation and discovery pipelines.
Basic Qualifications
- Education: Master's or PhD in Computer Science, AI, Machine Learning, or a related field.
- Experience: 5–8+ years of professional experience in AI/ML research or advanced engineering (industry experience preferred).
- Video Understanding / VLM Expertise: Deep, hands-on experience with video understanding, temporal modeling, and Multimodal Large Language Models (MLLMs/VLMs), including fine-tuning, post-training or adapting these models for production use cases.
- Frameworks: Expert proficiency in PyTorch, TensorFlow, or JAX.
- Production Skills: Proven track record of deploying large-scale AI models in production environments.
- SOTA-to-Production: Demonstrated ability to identify, evaluate, and rapidly adapt state-of-the-art techniques (from research papers, open-source releases, or industry advances) into working solutions that address real business needs.
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Preferred Qualifications
- Publications & Research Recognition: Contributions to high-impact projects or publications in top-tier conferences (e.g., CVPR, ICCV, NeurIPS).
- NLP & Content Assessment: Experience with content quality assessment, news stream analysis, or semantic modeling.
- Narrative & Aesthetics Analysis: Familiarity with high-level semantic modeling (e.g., emotion recognition, contextual reasoning), temporal action localization/ scene segmentation, and computational aesthetics or visual quality assessment.
- Technical Leadership: Experience leading engineers through complex technical challenges, for example as a technical lead on a project or workstream.
- Community Engagement: Experience presenting or mentoring within research communities, such as workshop talks, internal tech shares, or open-source maintainership.
Check out Life at Huawei Ireland Research Centre: https://www.youtube.com/watch?v=3gR64sYSnOA&feature=youtu.be
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