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.