Applied AI Researcher (Dublin, CA)

Articul8; Dublin; On-site; EUR 70,000 - 90,000; Full time

Job summary

Articul8 is looking for an Applied AI Researcher to enhance our GenAI platform in Dublin, Ireland. The role involves designing experiments, building evaluation pipelines, and integrating research into production with a focus on AI workflows.

Ideal candidates hold a PhD or MSc with significant research experience, possess strong foundations in AI techniques, and have proven software engineering skills. You'll work collaboratively to advance AI applications in various domains.

Qualifications

• Strong understanding of probability, optimization, and linear algebra.
• Experience building training or evaluation pipelines for real data.
• Production-quality Python coding skills and collaborative development experience.

Responsibilities

• Design and run AI research workflows using agentic systems.
• Train and iterate models across the GenAI stack.
• Build research infrastructure and integrate into production.

Skills

Model development

Technical foundations

Infrastructure

Software engineering

Education

PhD in Computer Science or related field

MSc with 4+ years research experience

Tools

Python

Git

Distributed training frameworks

Job description

About Us

Articul8 was born from a simple belief: GenAI should work for the enterprise, not the other way around. Our platform — combining domain-specific models, autonomous agentic reasoning (ModelMesh™), reliable model evaluation (LLM-IQ™), and multimodal understanding — serves regulated industries such as energy, semiconductor, finance, aerospace, supply chain, and more. Trusted by Fortune 500 enterprises, we bring together research, engineering, product, and domain expertise to deliver AI that meets the accuracy, explainability, and auditability standards that high‑stakes environments demand.

Job Description

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.

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

Required Qualifications

• Education: PhD in Computer Science, Machine Learning, or a related field; or MSc with 4+ years of post‑graduation research experience.

• Model development: 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.

• Technical foundations: 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.

• Infrastructure: Experience building training or evaluation pipelines that handle real data — preprocessing, distributed computation, experiment tracking, and reproducibility.

• Software engineering: Production‑quality Python. You write code others can read, test, and extend. Fluent with Git and collaborative development workflows.

Preferred Qualifications

• 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.

Professional Attributes (Code42)

• Practice Humility: You ask questions even when you think you know the answer. You seek feedback early, learn from anyone regardless of title, and treat every experiment — especially the failures — as data.

• Bias for Outcomes: You measure your work by what changed, not what you tried. You ship results, not slide decks. When a deadline is real, you find a way.

• Care Deeply: You treat every problem as yours to solve. You review your own work with the rigor you'd want from a reviewer. You help teammates without being asked.

• Dare to Do the Impossible & Embrace Scarcity: You set goals that make you uncomfortable. When told something can't be done, you find a way or a better question. Constraints sharpen your thinking, not slow it down.

• Build a Better World: You believe AI should make things meaningfully better for real people. You hold yourself accountable not just for whether your model works, but for what it does in the world.