AI Research Scientist

Lexsi Labs; Mumbai; On-site; INR 1,200,000 - 2,000,000; Full time

Job summary

Lexsi Labs in Mumbai is looking for an AI Research Scientist to advance research in agent learning, reinforcement learning, interpretability, and alignment. You will design algorithms and publish research while collaborating on next-generation AI systems. Required qualifications include a PhD or Master's degree in relevant fields, a strong background in reinforcement learning, and hands-on experience with model training using frameworks like PyTorch, JAX, or TensorFlow. Join us in building safe and interpretable AI systems.

Qualifications

• PhD or Masters or equivalent research experience in Machine Learning, AI, Computer Science, Mathematics, or Physics.
• Strong background in reinforcement learning, multi-agent systems, and interpretability.
• Proven ability to publish in top-tier AI conferences.

Responsibilities

• Conduct foundational research on multi-agent reinforcement learning and alignment strategies.
• Design and evaluate algorithms for learning efficiency and controllability of AI systems.
• Develop methods for interpreting and analyzing internal model behavior.

Skills

Reinforcement learning

Machine learning

Deep learning theory

Interpretability

Information-theoretic ML

Education

PhD or Master's in relevant field

Tools

PyTorch

JAX

TensorFlow

Job description

Lexsi Labs

Lexsi Labs is a frontier AI research lab focused on building safe, interpretable, and aligned AI systems as capabilities scale toward advanced autonomous intelligence.

Our research operates at the intersection of agent learning, alignment, and interpretability, with the goal of developing the scientific foundations required for trustworthy AI systems.

Key Research Areas

• Multi-agent reinforcement learning and agent learning systems for scalable coordination, exploration, and long-horizon learning.
• Continual learning and adaptive agents that can evolve safely over time without catastrophic forgetting.
• Alignment strategies and behavioral control methods including reward modeling, safety fine-tuning, pruning, and unlearning.
• Mechanistic interpretability and model transparency to understand internal representations and reasoning behavior in large models.
• Information-theoretic approaches such as mutual information and causal analysis to study how knowledge and decisions form inside deep networks.
• Foundation models for structured and tabular domains aimed at high-stakes real-world applications.
• Open research infrastructure and tooling for alignment experimentation, RL training, and model evaluation.
• Open science and global collaboration, with research hubs in Mumbai and Paris and publications in leading venues such as ICML, ACL, IJCNN, and WWW.

Our goal is to advance the science required to ensure that future AI systems remain transparent, controllable, and aligned with human intent.

The Role

As an AI Research Scientist, you will work on fundamental research problems at the frontier of agent learning, reinforcement learning, interpretability, and alignment.

Your work will involve designing new algorithms, studying the internal behavior of large models, building research systems for experimentation, and contributing to open research that advances the understanding and safety of AI systems.

This role offers the opportunity to pursue ambitious research ideas while collaborating with a team building the next generation of alignment methods and agent learning systems.

Responsibilities

• Conduct foundational research on multi-agent reinforcement learning, agent exploration, continual learning, and scalable alignment strategies.
• Design and evaluate algorithms that improve the learning efficiency, coordination, and controllability of large AI systems.
• Develop methods for interpreting and analyzing internal model behavior, including representation analysis, circuit discovery, and causal reasoning tools.
• Investigate alignment techniques such as reward modeling, safety fine-tuning, pruning, unlearning, and training-time behavioral control.
• Explore information-theoretic and causal approaches for understanding how models represent knowledge and make decisions.
• Build research infrastructure and experimental frameworks for RL training, alignment experiments, and large-scale evaluation.
• Publish research in leading venues and contribute to open-source tools and alignment research infrastructure.

Ideal Qualifications

• PhD or Masters or equivalent research experience in Machine Learning, Artificial Intelligence, Computer Science, Mathematics, Physics, or related fields.
• Strong background in reinforcement learning, multi-agent systems, large language models, interpretability, deep learning theory, or information-theoretic ML.
• Proven ability to conduct independent research and publish in top-tier AI conferences such as NeurIPS, ICML, ICLR, ACL, CVPR, or AAAI.
• Hands‑on experience training and experimenting with models using frameworks such as PyTorch, JAX, or TensorFlow.