Company: Antal International

Website: Visit Website

Business Type: Startup

Company Type: Product

Business Model: B2B

Funding Stage: Pre-seed

Industry: Information Technology

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–setting–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

- Solid 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.

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