Agentic RL Researcher for Personal Shopping AI

KAISHI PARTNERS PTE. LTD.; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

KAISHI PARTNERS PTE. LTD. in Singapore is seeking researchers to advance a personal shopping AI agent. You will design RL experiments, build evaluation frameworks, and translate research into actionable product behaviours that improve recommendations and user satisfaction.

You will work closely with engineers to shape learning objectives, collect usable data, and rigorously test new methods in a production-like environment.

Qualifications

• Deep knowledge of reinforcement learning and hands-on research or implementation experience.
• Strong Python and ML engineering skills.
• Experience designing experiments and evaluating results.
• Ability to translate an open-ended product problem into a tractable research question.
• Interest in working closely with engineers and learning from real product behaviour.
• Evidence of research depth through papers, experiments, implementations, or deployed systems.

Responsibilities

• Investigate reward design and credit assignment for delayed outcomes such as satisfaction, returns, and repeat use.
• Develop approaches to user modelling, memory, and adaptation as preferences change.
• Explore policies for when an agent should ask, recommend, act, or wait.
• Build reproducible experiments, meaningful baselines, and ablation studies.
• Evaluate policy improvements critically, including uncertainty and unintended behaviours.
• Collaborate with engineers on data and instrumentation for research.
• Translate findings into product behaviours and testing.

Skills

Reinforcement learning

Python

Experiment design

Critical evaluation

Research depth

Tools

Python

Job description

KAISHI PARTNERS PTE. LTD. in Singapore is seeking researchers to advance a personal shopping AI agent. You will design RL experiments, build evaluation frameworks, and translate research into actionable product behaviours that improve recommendations and user satisfaction.

You will work closely with engineers to shape learning objectives, collect usable data, and rigorously test new methods in a production-like environment.