Artificial Intelligence Researcher

Company: Antal International

Website: Visit Website

Business Type: Startup

Company Type: Product

Business Model: B2B

Funding Stage: Pre-seed

Industry: Information Technology

Job Description

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.
  
  

• 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.
  
  

• 2–6+ years in data science, applied research, or ML engineering roles.
• Proven hands-on experience with deep learning frameworks and reinforcement learning projects.
  
  

• 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.
  
  

• 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.