Applied AI Researcher (Dublin, CA)

Articul8 • Dublin, CA

2d • Onsite

About The Position

Articul8 AI is seeking an Applied AI Researcher to advance our domain-specific GenAI platform. You will design and run experiments, build training and evaluation pipelines, and ship research into production. This role spans model training, reinforcement learning, multimodal understanding, and knowledge representation.

Requirements

• PhD in Computer Science, Machine Learning, or a related field; or MSc with 4+ years of post-graduation research experience.
• You have trained or fine-tuned at least one neural model end-to-end — data preparation through evaluation. You understand why your model converges or doesn't, not just how to launch a training run.
• Strong working knowledge of probability, optimization, and linear algebra applied to at least one of: NLP, computer vision, reinforcement learning, or information retrieval. You can derive the math behind the methods you use.
• Experience building training or evaluation pipelines that handle real data — preprocessing, distributed computation, experiment tracking, and reproducibility.
• Production-quality Python. You write code others can read, test, and extend. Fluent with Git and collaborative development workflows.

Nice To Haves

• Experience with distributed training frameworks (PyTorch DDP, DeepSpeed, FSDP) — you understand gradient synchronization and can debug multi-GPU failures.
• Published at NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, or equivalent. Quality of contribution matters more than count.
• Hands-on experience with post-training methods (RLHF, DPO, reward modeling) — beyond reading papers.
• Practical cloud infrastructure experience (AWS, GCP, or Azure) for ML workloads — you can provision resources, manage jobs, and troubleshoot training failures.

Responsibilities

• Architect and orchestrate massively parallel AI research workflows — design experiments that leverage fleets of agentic AI systems to explore hypothesis spaces, hyperparameter landscapes, and architectural variations at a scale and speed no single researcher could achieve alone
• Design, train, and iterate on models across the full GenAI stack — LLMs, VLMs, embedding models, rerankers, and reward models — using agentic pipelines that autonomously manage data preprocessing, training runs, evaluation sweeps, and result synthesis
• Go deep: push the frontier of domain-specific AI — conduct rigorous, first-principles research into model architectures, training dynamics, reinforcement learning, and knowledge representation, using AI agents to accelerate literature review, ablation studies, and mathematical analysis
• Go broad: span disciplines and modalities — amplify your expertise across NLP, computer vision, multimodal understanding, agentic reasoning, and domain science by delegating exploration, prototyping, and benchmarking to parallel agent systems so you can synthesize insights across fields simultaneously
• Build agentic research infrastructure — develop and contribute to shared tooling, libraries, and platforms that enable every researcher on the team to orchestrate autonomous experiment pipelines, data processing workflows, and evaluation harnesses at scale
• Ship research into production at velocity — collaborate with engineering, product, and domain experts to integrate breakthroughs into the platform rapidly, using agentic CI/CD and automated integration testing to compress the research-to-deployment cycle
• Amplify collective intelligence — document findings, publish at top-tier venues, and build internal knowledge systems that agentic tools can index and reason over — turning every insight into a force multiplier for the entire team
• Continuously raise the ceiling on human potential — proactively identify bottlenecks in your own workflow and the team's, then design or adopt efficient, scalable solutions that eliminate them — treating your own augmentation as a core research output

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree