Location: Dublin, Ireland
About The Role
We are looking for a Senior Machine Learning Engineer to drive the development of our next-generation content intelligence systems.
In this role, you will be a key technical contributor in developing complex multimodal understanding solutions across video, audio, image, and text.
Leveraging cutting-edge Multimodal Large Language Models (MLLMs), you will build and optimize a self-evolving content analysis ecosystem.
Your work will be instrumental in enhancing our core recommendation engine, directly impacting product performance and user engagement through advanced content signals.
Key Responsibilities
Develop & optimize multimodal frameworks focused on semantic understanding, narrative reasoning, and high-level content interpretation.
Own end-to-end development of high-performance systems that drive algorithmic innovation and resolve high-impact technical bottlenecks.
Work closely with cross-functional teams to integrate content signals into production recommendation and discovery pipelines.
Ensure models are accurate and optimized for low-latency inference and high-concurrency production environments.
Requirements
Education: Master's or PhD in Computer Science, AI, Machine Learning, or a related quantitative field.
Experience: 5–8+ years of professional experience in AI/ML research or advanced engineering (industry experience preferred).
Technical Depth: Strong hands-on experience in at least two of the following:
Multimodal learning – integration of video, text, image, and audio signals.
Computer vision – video understanding, temporal modeling, or representation learning.
NLP – semantic modeling, content quality assessment, or news stream analysis.
Frameworks: Expert proficiency in PyTorch, TensorFlow, or JAX.
MLLM/VLM Expertise: Practical experience working with or fine-tuning Multimodal Large Language Models.
Production Skills: Proven track record of deploying large-scale AI models in production environments.
Preferred Qualifications
Track record of contributions to high-impact projects or publications in top-tier conferences (e.g., CVPR, ICCV, NeurIPS).
System Design: Experience building automated content tagging or recommendation signals from the ground up.
Domain Knowledge: Familiarity with digital signal processing for audio or aesthetic evaluation for images/video.
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