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Senior Applied AI Researcher (Dublin, CA)

Articul8· Applied Research
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Dublin, CA (HQ)Full-timePosted 5mo ago

About the role

Senior AI researcher role focusing on open research problems within Articul8's domain-specific GenAI platform, spanning model training, reinforcement learning, multimodal understanding, and knowledge representation with focus on production-ready research outcomes.

Responsibilities

  • Own and orchestrate end-to-end research programs using massively parallel agentic AI — from problem formulation through production deployment, designing agent-driven experiment campaigns that explore architectures, training regimes, data strategies, and evaluation criteria.
  • Go deep: drive breakthrough domain-specific model quality — lead multi-stage training pipelines, domain adaptation, RL-based optimization (RLHF, DPO, reward modeling), and training dynamics analysis with parallel ablations and hyperparameter sweeps.
  • Go broad: design and train multimodal systems (text, images, tables, charts, technical documents), knowledge graph pipelines, hybrid retrieval architectures, and structured reasoning systems with parallel agent workflows to synthesize cross-cutting insights in real time.
  • Architect agentic data and training infrastructure — build agent-orchestrated pipelines for domain-specific data curation, preprocessing, and large-scale training.
  • Mentor AI Researchers in the agentic paradigm — coach team members on agent workflows and expanding research depth.
  • Compress the research-to-production cycle — leverage agentic CI/CD, automated integration testing, and continuous evaluation with collaboration across engineering, product, and domain experts.
  • Build force-multiplying knowledge systems — document findings, publish at top-tier venues, and contribute to internal knowledge infrastructure that agentic tools can index and reason over.
  • Model the augmented researcher — identify bottlenecks and design scalable solutions to maximize human potential as a research output.

Qualifications

  • Education: PhD or MSc in Computer Science, Machine Learning, or a related field.
  • Experience: 5+ years as an AI/ML researcher with shipped research artifacts (models, systems, or tools in production), including 2+ years building LLM-based systems.
  • Model training depth: experience with multi-stage training pipelines (pretraining, fine-tuning, post-training) and diagnosing training failures from loss curves, gradient norms, and evaluation metrics.
  • Technical specialization: deep expertise in at least one of domain-specific model adaptation, multimodal learning, reinforcement learning from human feedback, knowledge-grounded generation, or retrieval-augmented systems; publication or production work in the area.
  • Distributed systems: hands-on experience with distributed training at scale (e.g., DeepSpeed, FSDP, Megatron-LM); understanding data vs model parallelism.
  • Software engineering: production-grade Python, clean abstractions, tested code; building tools others depend on.

Skills

Required:Agentic research workflowsMultimodal systems
Nice to have:Knowledge graphs / retrieval-augmented systems
Last updated Aug 5, 2026. Interested? Apply on the company site.
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