Applied AI Researcher

Location: Dublin, California, United States

Company

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

What you will do

• Architect massively parallel AI research workflows using fleets of agentic systems to explore hypotheses, hyperparameters, and model architectures.
• Design, train, and iterate on LLMs, VLMs, embedding models, rerankers, and reward models across the full GenAI stack.
• Conduct research into model architectures, training dynamics, reinforcement learning, knowledge representation, NLP, computer vision, and multimodal understanding.
• Build shared tooling, libraries, experiment pipelines, data workflows, and evaluation harnesses for scalable research.
• Collaborate with engineering, product, and domain experts to integrate research into production using automated testing and CI/CD.
• Document findings, publish research, and develop internal knowledge systems that improve collective research capabilities.

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 experience with Git and collaborative software development workflows.

Nice to have

• Experience with PyTorch DDP, DeepSpeed, FSDP, or other distributed training frameworks.
• 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

• Emphasis on humility, early feedback, continuous learning, and treating experiments and failures as data.
• Outcome-oriented environment focused on shipping measurable results.
• Encouragement to take ownership, solve difficult problems, and work effectively under constraints.
• Focus on developing AI that creates meaningful value for people and high-stakes industries.