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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