# Research Scientist - RLHF, RLAIF & Reward Modeling

> Weekday AI · Bengaluru, India · Full-time · Posted 2026-10-01

**Salary:** INR 5,000,000–10,000,000

**Workplace:** on_site

**Department:** Weekday's Client via platform

## Description

**This role is for one of Weekday’s clients  
Salary range: Rs 5000000 - Rs 10000000 (ie INR 50 - 100 LPA)**

  
Min Experience: 3+ years  
Location: Bengaluru, Karnataka, India  
JobType: full-time

We are looking for a highly skilled and research-oriented **Research Scientist** with 3–6 years of experience in machine learning, reinforcement learning, and large language model (LLM) alignment. The ideal candidate will have strong hands-on experience with **Reinforcement Learning from Human Feedback (RLHF), Reinforcement Learning from AI Feedback (RLAIF), and Reward Modeling**, and will contribute to developing and improving advanced AI systems.

You will work on research problems related to model alignment, preference learning, reward optimization, evaluation, and post-training. This role requires a strong understanding of modern machine learning techniques, the ability to translate research ideas into working systems, and experience conducting rigorous experiments on large-scale models.

## Requirements

Key Responsibilities

-   Design, implement, and evaluate **RLHF pipelines** for training and aligning large language models with human preferences.
-   Develop and improve **RLAIF methodologies** using AI-generated feedback, preference signals, and automated evaluation frameworks.
-   Build, train, and validate **reward models** that accurately capture human or AI preferences and desired model behaviors.
-   Experiment with reinforcement learning and preference optimization techniques to improve model helpfulness, accuracy, safety, and instruction following.
-   Analyze model behavior and training outcomes using quantitative evaluations, benchmarks, and controlled experiments.
-   Develop data-generation, preference-collection, ranking, and annotation strategies for alignment and post-training datasets.
-   Collaborate with research engineers and ML engineers to scale training and experimentation pipelines.
-   Investigate failure modes in reward models, preference datasets, and alignment techniques, and propose research-driven solutions.
-   Stay current with emerging research in LLM alignment, reinforcement learning, preference learning, reward modeling, and AI feedback.
-   Document experimental results and communicate research findings clearly through technical reports, presentations, and research papers.

Must-Have Skills

-   **3–6 years of hands-on experience** in machine learning, deep learning, reinforcement learning, or a closely related research field.
-   Strong practical experience with **RLHF (Reinforcement Learning from Human Feedback)**.
-   Strong understanding and hands-on experience with **RLAIF (Reinforcement Learning from AI Feedback)**.
-   Proven experience developing, training, or evaluating **reward models** and preference-based learning systems.
-   Strong understanding of reinforcement learning concepts, policy optimization, reward functions, preference modeling, and model evaluation.
-   Experience working with **Large Language Models (LLMs)** and their training or post-training workflows.
-   Strong Python programming skills and experience with modern deep learning frameworks such as **PyTorch** or equivalent.
-   Ability to design experiments, interpret results, troubleshoot training issues, and derive meaningful research insights.
-   Strong mathematical and statistical foundations relevant to machine learning and reinforcement learning.

Good-to-Have Skills

-   Experience with PPO, DPO, GRPO, or other reinforcement learning and preference optimization techniques.
-   Experience working with transformer architectures and LLM fine-tuning.
-   Familiarity with distributed model training and large-scale experimentation.
-   Experience publishing research papers or contributing to open-source ML research.
-   Knowledge of model evaluation, red-teaming, AI safety, or alignment research.

Qualifications

A Master’s or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, Mathematics, Statistics, or a related technical field is preferred. Candidates with strong industry research experience and demonstrated expertise in RLHF, RLAIF, and reward modeling are encouraged to apply.

## Apply

[Apply at Weekday AI](https://apply.workable.com/weekday-1/j/00DE8D2969/apply)

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