New opportunity

AI Research Scientist

AryaXAI · Bangalore, India

About this role

The AI Research Scientist will investigate fundamental problems in agent learning, reinforcement learning, interpretability and AI alignment. The posting describes research aimed at making advanced AI systems transparent, controllable and aligned with human intent. The role is listed as hybrid in Bangalore, India, and full-time.

Responsibilities

Conduct foundational research in multi-agent reinforcement learning, exploration, continual learning and scalable alignment. Design and evaluate algorithms to improve learning efficiency, coordination and controllability in large AI systems.
Study internal model behavior through representation analysis, circuit discovery, causal reasoning and information-theoretic methods. Investigate reward modeling, safety fine-tuning, pruning, unlearning and training-time behavioral control.
Build experimental frameworks and research infrastructure for reinforcement learning training, alignment experiments and large-scale evaluation. Publish findings and contribute to open-source tools and alignment research infrastructure.

Ideal qualifications:

A PhD, master's degree or equivalent research experience in machine learning, artificial intelligence, computer science, mathematics, physics or a related field.
A strong background in one or more of reinforcement learning, multi-agent systems, large language models, interpretability, deep learning theory or information-theoretic machine learning. The posting seeks demonstrated independent research and publication ability at leading AI conferences.
Hands-on model training and experimentation using PyTorch, JAX or TensorFlow; strong Python programming and modern ML research-tooling skills; and an interest in AI alignment, safety, interpretability or trustworthy AI.

The research context also includes adaptive agents, foundation models for structured and tabular data, and open collaboration. The application asks candidates about their research interests, publications, total work experience and ML/DL experience, but states no minimum number of years or salary.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Reinforcement LearningMulti-Agent SystemsContinual LearningAI AlignmentMechanistic InterpretabilityLarge Language ModelsDeep LearningInformation-Theoretic Machine LearningCausal AnalysisReward ModelingSafety Fine-TuningModel PruningMachine UnlearningRepresentation AnalysisPythonPyTorchJAXTensorFlowResearch ExperimentationScientific PublishingOpen-Source Development

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Advance agent-learning algorithms
The role calls for foundational work in multi-agent reinforcement learning, exploration and continual learning, with algorithms evaluated for learning efficiency, coordination and controllability.
Explain internal model behavior
Develop interpretability methods using representation analysis, circuit discovery and causal reasoning; the posting also names information-theoretic approaches to understanding model decisions.
Investigate alignment and behavioral control
Research reward modeling, safety fine-tuning, pruning, unlearning and training-time control as ways to make advanced systems more aligned with human intent.
Build rigorous experimental infrastructure
Create frameworks for RL training, alignment experiments and large-scale evaluation, supported by hands-on model training and Python-based research tooling.
Produce open, publishable research
The responsibilities include publishing findings in leading venues and contributing to open-source tools and alignment research infrastructure; the ideal qualifications emphasize independent research.

Likely challenges to think through

Balance capability gains with controllability
Likely preparation challenge: an agent-learning method may improve exploration or coordination without improving behavioral control. Prepare to explain how you would evaluate both, including failure cases rather than only headline performance.
Turn interpretability findings into causal evidence
Likely preparation challenge: a representation pattern or discovered circuit may correlate with behavior without explaining it. Prepare an example of how interventions, controls and alternative explanations could test a proposed mechanism.
Make ambitious experiments reproducible
Likely preparation challenge: large-scale RL and alignment studies can be costly and variable, creating a trade-off between breadth of testing and experimental rigor. Prepare to discuss baselines, repeated trials and efficient evaluation choices.

Practice your answers

Technical practice

How would you design a multi-agent RL study to improve coordination?

What to coverThe role calls for algorithms that improve coordination in multi-agent systems. Suggested preparation: explain the agents’ objectives, coordination measure, baselines, and how you would distinguish genuine cooperation from gains caused by a particular experimental setup.

How would you evaluate an exploration method for long-horizon agent learning?

What to coverThe posting identifies agent exploration, long-horizon learning, and learning efficiency as research areas. Suggested preparation: discuss exploration–exploitation trade-offs, appropriate baselines, and measures of both performance and training cost.

How could an adaptive agent learn continually without catastrophic forgetting?

What to coverContinual learning and safe adaptation over time are stated research areas. Suggested preparation: compare approaches to retaining prior capabilities while learning new tasks, and explain how you would test both adaptation and forgetting.

How would you assess reward modeling and safety fine-tuning as alignment methods?

What to coverThe role includes reward modeling, safety fine-tuning, and behavioral control. Suggested preparation: describe what each method can influence, how you would evaluate intended behavior, and where optimizing a measured objective might fail to reflect human intent.

What trade-offs would you examine when using pruning or unlearning for behavioral control?

What to coverPruning and unlearning are explicitly listed among the alignment techniques to investigate. Suggested preparation: explain how you would check whether an unwanted behavior was reduced while testing for loss of useful capabilities or behavior that persists under different evaluations.

How would you test whether a discovered model circuit explains a behavior?

What to coverThe posting calls for representation analysis, circuit discovery, and study of internal model behavior. Suggested preparation: distinguish an observed correlation from a causal explanation, and describe interventions and checks against alternative explanations.

How might information-theoretic and causal analyses complement each other in studying model decisions?

What to coverThe role names mutual information and causal analysis as ways to study how knowledge and decisions form in deep networks. Suggested preparation: explain what each analysis can reveal, its limitations, and how an intervention could strengthen a claim about a representation’s role.

How would you evaluate a foundation model for structured or tabular data in a high-stakes setting?

What to coverThe source identifies foundation models for structured and tabular domains aimed at high-stakes applications as a research area. Suggested preparation: discuss meaningful baselines, data leakage, robustness, and the limits of drawing conclusions from aggregate performance alone.

How would you establish that a new algorithm improves learning efficiency and controllability?

What to coverDesigning and evaluating algorithms for both learning efficiency and controllability is a stated responsibility. Suggested preparation: define each outcome separately, compare against relevant baselines, use ablations, and account for differences in training resources.

How would you build a reproducible framework for RL and alignment experiments?

What to coverThe role includes research infrastructure for RL training, alignment experiments, and large-scale evaluation, alongside Python and model-training frameworks. Suggested preparation: explain experiment configuration, repeatability, failure diagnosis, and how you would make results comparable across runs.

Behavioural practice

Tell me about a research question you pursued independently from hypothesis to conclusion.

What to coverThe posting seeks independent research ability. Suggested preparation: use a real situation, describe the decisions and work you owned, then give the result and what the evidence did or did not establish.

Describe a time an experiment contradicted your hypothesis or was difficult to reproduce.

What to coverThe role involves designing and evaluating research experiments. Suggested preparation: give the situation, the checks or changes you made, and the result, including how the finding affected your next research decision.

Tell me about a time you collaborated across different research perspectives.

What to coverThe source describes team collaboration and open, global research. Suggested preparation: identify the shared problem, your specific actions to resolve a substantive disagreement or coordination challenge, and the resulting work or decision.

Describe how you communicated a research result for publication or open-source use.

What to coverPublishing findings and contributing to open-source research tools are stated responsibilities. Suggested preparation: explain the audience and situation, what you did to make the evidence or tool understandable and usable, and the actual outcome.

Tell me about a time safety or interpretability concerns changed a research decision.

What to coverThe role focuses on transparent, controllable, aligned AI systems. Suggested preparation: use a real situation to explain the concern, the action you took or recommended, and the result without claiming a safety outcome you did not verify.

Sharpen your knowledge

Topics to prepare

Multi-agent reinforcement learning
Suggested revision exercise: Sketch a two-agent learning problem and explain how you would measure coordination, exploration and learning efficiency over long horizons. Relevance: the role explicitly calls for research on multi-agent RL and agent exploration.
Continual learning and adaptive agents
Suggested revision exercise: Explain how an agent could learn a new task without catastrophic forgetting, then define a test for retained performance on earlier tasks. Relevance: continual learning and safe adaptation are named research areas.
Alignment and behavioral control
Suggested revision exercise: Compare reward modeling, safety fine-tuning, pruning and unlearning by the behavior each aims to change and how you would test controllability. Relevance: the responsibilities explicitly name these techniques.
Mechanistic interpretability
Suggested revision exercise: Outline how representation analysis, circuit discovery and a causal intervention could support—or weaken—a claim about a model’s internal behavior. Relevance: interpreting large-model representations and reasoning is a stated responsibility.
Information-theoretic and causal analysis
Suggested revision exercise: Formulate a hypothesis about how a model represents information; explain what mutual information could measure and what additional intervention would be needed for a causal claim. Relevance: both approaches are explicitly listed for studying knowledge and decisions in deep networks.

Skills to revise

Python model-training experiments
Suggested hands-on exercise: Train a small model in Python using one of the named frameworks—PyTorch, JAX or TensorFlow—and record configurations, results and failure cases. Relevance: the posting seeks hands-on model training and strong Python experimentation skills; it does not require all three frameworks.
RL algorithm evaluation
Suggested hands-on exercise: Implement a baseline and one modification in a small multi-agent setting; compare learning efficiency and coordination across repeated runs. Relevance: the role involves designing and evaluating algorithms for agent learning.
Internal-behavior analysis
Suggested hands-on exercise: Inspect model activations, propose an interpretation of a representation, and test it by changing an input or intervening on the model. Relevance: representation analysis and causal reasoning tools are explicit responsibilities.
Alignment experiment design
Suggested hands-on exercise: Design a controlled comparison of one named alignment method against an unchanged model, specifying intended behavior, evaluation criteria and possible adverse effects. Relevance: the role includes alignment experiments and training-time behavioral control.
Research reporting and reusable tooling
Suggested hands-on exercise: Turn an experiment into a concise research report with a question, baselines, methods, results and limitations; package the code so another researcher could rerun it. Relevance: the posting calls for publications, experimental infrastructure and open-source contributions.

Your preparation checklist

Select a research focus
The posting spans agent learning, interpretability and alignment. Prepare a short statement naming one problem you would pursue over the next 1–2 years, why it matters, and a testable first experiment; the application asks for a preferred research area.
Build a project walkthrough
The role calls for independent research and algorithm evaluation. Prepare one relevant project walkthrough covering the problem, your contribution, baselines, results, limitations and next experiment. Use an actual project, not a hypothetical claim.
Organize evidence of research output
The posting seeks publication ability and asks for an A* paper count. Prepare an accurate publication list, with your contribution to each work, and identify other research outputs you can discuss where relevant.
Review a training experiment
The role calls for hands-on model training with PyTorch, JAX or TensorFlow and strong Python experimentation skills. Be ready to explain a real experiment’s implementation, evaluation choices, reproducibility measures and failures.
Practice a research design exercise
As interview preparation, sketch a hypothetical study in either multi-agent coordination, continual learning or alignment control. Specify a baseline, success measures, ablations and a safety or interpretability check; these are preparation suggestions, not a stated interview task.
Prepare a model-analysis explanation
The responsibilities include representation analysis, circuit discovery and causal reasoning. Choose a method you know and prepare to explain what evidence it provides, how you would test a causal claim, and where the method could mislead.
Draft application answers and employer questions
The form asks about a problem solved in a current role, research preferences, experience and publications; draft precise answers where applicable. Ask how AryaXAI and the Lexsi Labs research description relate, which research direction this role prioritizes, and whether the working location is Bangalore or Mumbai, since the page presents both.

Availability last checked: 5 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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