This is a Permanent role with a valued client of Antal International
We’re looking for a passionate AI researcher to advance agentic reasoning and decision intelligence systems. This role bridges cutting-edge research and real-world AI applications, ideal for someone who thrives on open-ended problems and scientific innovation.
What You’ll Do
• Conduct core scientific research to improve agentic reasoning reliability and learning.
• Define agent–environment–reward formulations for decision intelligence workflows.
• Frame learning problems using Deep Reinforcement Learning, preference learning, or supervised fine-tuning.
• Design and evaluate reasoning paradigms such as Chain-of-Thought, Tree-of Thought, and multi-step planning.
• Curate datasets for training and evaluating reasoning agents.
• Contribute to knowledge system learning, including graph updates and ontology refinement.
• Collaborate closely with AI Engineers to translate research outcomes into production systems.
What We’re Looking For
Education
Bachelor’s or Master’s in Computer Science, AI, Data Science, or a related field.
Advanced degrees or a strong academic research background are preferred.
Professional Experience
2–6+ years in data science, applied research, or ML engineering roles.
Proven hands-on experience with deep learning frameworks and reinforcement learning projects.
Technical Skills
Strong proficiency in PyTorch and TensorFlow.
Working knowledge of reinforcement learning algorithms such as MDPs, PPO, DPO, GRPO, etc.
Experience with transformers and LLM fine-tuning (SFT, LoRA, QLoRA).
Solid understanding of classical machine learning and statistical learning concepts.
Excellent analytical and experimental design skills, with the ability to evaluate AI agents rigorously.
Personal Attributes
Strong scientific curiosity and a passion for exploring challenging problems.
Ability to balance deep research with practical impact, delivering meaningful results.
Comfortable working on open-ended, complex problems in a collaborative environment.