Applied AI Researcher

Location: Dublin, California, United States (HQ)

Company

hirify.global develops a domain-specific GenAI platform with agentic reasoning, model evaluation, and multimodal understanding for regulated industries.

What you will do

• Architect massively parallel AI research workflows for experiments, hypothesis exploration, hyperparameter sweeps, and architecture evaluation.
• Design, train, and iterate on LLMs, VLMs, embedding models, rerankers, and reward models.
• Conduct research into model architectures, training dynamics, reinforcement learning, and knowledge representation.
• Explore NLP, computer vision, multimodal understanding, agentic reasoning, and domain science through parallel agent systems.
• Build shared tooling, libraries, experiment pipelines, data workflows, and evaluation harnesses.
• Collaborate with engineering, product, and domain experts to integrate research into production and document or publish findings.

Requirements

• PhD in Computer Science, Machine Learning, or a related field, or an MSc with 4+ years of post-graduation research experience.
• Experience training or fine-tuning at least one neural model end-to-end, from data preparation through evaluation.
• Strong knowledge of probability, optimization, and linear algebra applied to NLP, computer vision, reinforcement learning, or information retrieval.
• Experience building training or evaluation pipelines involving real data, preprocessing, distributed computation, experiment tracking, and reproducibility.
• Production-quality Python and proficiency with Git and collaborative development workflows.

Nice to have

• Experience with PyTorch DDP, DeepSpeed, or FSDP and debugging multi-GPU failures.
• Publications at NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, or equivalent venues.
• Hands-on experience with RLHF, DPO, or reward modeling.
• Cloud infrastructure experience with AWS, GCP, or Azure for machine learning workloads.

Culture & Benefits

• Work in a research environment focused on measurable outcomes and production impact.
• Collaborate across research, engineering, product, and domain disciplines.
• Practice early feedback, humility, rigorous review, and learning from failed experiments.
• Develop AI intended to meet accuracy, explainability, and auditability standards in high-stakes environments.