New opportunity

AI Research Scientist

AryaXAI · Bangalore, India

About this role

AryaXAI seeks an AI Research Scientist for a hybrid, full-time Research & Development role in Bangalore. The role focuses on fundamental research in agent learning, reinforcement learning, interpretability and alignment to advance safe, transparent and controllable AI systems. The posting describes research conducted through Lexsi Labs, with research hubs in Mumbai and Paris.

Responsibilities

Research multi-agent reinforcement learning, agent exploration, continual learning and scalable alignment. Design and evaluate algorithms that improve learning efficiency, coordination and controllability in large AI systems.
Study internal model behavior through representation analysis, circuit discovery and causal reasoning. Investigate reward modeling, safety fine-tuning, pruning, unlearning and training-time behavioral control, as well as information-theoretic and causal approaches to model knowledge and decisions.
Build experimental frameworks and research infrastructure for reinforcement-learning training, alignment experiments and large-scale evaluation. Publish research 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 with frameworks such as PyTorch, JAX or TensorFlow; strong Python programming and modern ML research-tooling skills; and interest in AI alignment, safety, interpretability or trustworthy AI.

Related research areas include adaptive agents that avoid catastrophic forgetting, foundation models for structured and tabular data, and open tools for alignment experimentation. The application asks candidates about their preferred research area, publications and experience, but specifies no minimum years of experience 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 reinforcement learningMulti-agent systemsAgent learningContinual learningAI alignmentAI safetyMechanistic interpretabilityRepresentation analysisCircuit discoveryCausal analysisInformation theoryReward modelingSafety fine-tuningModel pruningMachine unlearningLarge language modelsDeep learningFoundation modelsModel evaluationPyTorchJAXTensorFlowPythonIndependent researchResearch publication

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

Agent-learning research
The role calls for foundational work in multi-agent reinforcement learning, exploration and continual learning, including algorithms that improve learning efficiency, coordination and controllability.
Interpreting model behavior
Responsibilities include studying internal representations and reasoning through representation analysis, circuit discovery, causal reasoning and information-theoretic approaches.
Alignment and behavioral control
The posting names reward modeling, safety fine-tuning, pruning, unlearning and training-time behavioral control as techniques to investigate.
Experimental systems and evaluation
The scientist is expected to build research infrastructure and experimental frameworks for RL training, alignment experiments and large-scale evaluation.
Independent, open research
The role includes publishing research and contributing to open-source tools. The ideal qualifications emphasize independent research and publication ability at leading AI conferences.

Likely challenges to think through

Coordinating agents without sacrificing efficiency
A likely research trade-off is that stronger exploration or coordination can increase training cost or complicate long-horizon evaluation; continual learning also raises the risk of forgetting. Prepare to explain baselines and measurements for coordination, efficiency and retention. This is a preparation hypothesis, not a reported problem at AryaXAI.
Testing whether control methods work as intended
A plausible difficulty is showing that fine-tuning, pruning or unlearning changes targeted behavior without obscuring unintended effects. Prepare to discuss controlled comparisons, behavioral evaluations and limitations of the evidence. This is an inference from the listed research areas.
Distinguishing causal insight from correlation
Representation patterns or apparent circuits may not, on their own, explain a model’s decisions. Prepare to distinguish observational analysis from intervention-based tests and to discuss where conclusions may not generalize. This is a research challenge inferred from the interpretability duties.

Practice your answers

Technical practice

How would you evaluate a new multi-agent reinforcement-learning method for coordination?

What to coverThe role calls for algorithms that improve coordination and learning efficiency. Suggested preparation: compare against appropriate baselines, measure performance as the number of agents grows, and discuss how you would detect coordination failures.

How would you improve exploration for an agent learning over a long horizon?

What to coverAgent exploration and long-horizon learning are stated research areas. Suggested preparation: explain the exploration problem, a plausible algorithmic approach, and how you would distinguish genuine learning gains from extra training or evaluation variance.

How would you test whether an adaptive agent learns new tasks without catastrophic forgetting?

What to coverThe posting explicitly links continual learning with avoiding catastrophic forgetting. Suggested preparation: describe a sequence-of-tasks evaluation, measure both retention and adaptation, and discuss trade-offs between them.

How would you compare reward modeling with safety fine-tuning as alignment methods?

What to coverBoth are named alignment techniques in the role. Suggested preparation: explain what each changes in training, how you would evaluate intended behavior and unintended effects, and where the methods might complement rather than replace each other.

How would you assess whether pruning or unlearning produced the intended behavioral change?

What to coverThe role includes pruning, unlearning and behavioral control. Suggested preparation: define what should change and what should remain intact, then discuss tests for residual behavior, performance loss and misleading apparent success.

How would you investigate whether a discovered model circuit causally supports a behavior?

What to coverRepresentation analysis, circuit discovery and causal reasoning tools are stated responsibilities. Suggested preparation: distinguish an observed correlation from a causal claim, describe an intervention-based test, and acknowledge limits of the interpretation.

What can mutual information and causal analysis each reveal about a model's internal representations?

What to coverThe posting names both approaches for studying how knowledge and decisions form in deep networks. Suggested preparation: explain the question each method can address, the assumptions behind an interpretation, and why statistical dependence alone does not establish causation.

How would you design an experimental framework for reproducible RL and alignment research?

What to coverBuilding infrastructure for RL training, alignment experiments and large-scale evaluation is a stated duty; Python and frameworks such as PyTorch, JAX or TensorFlow are listed qualifications. Suggested preparation: discuss experiment configuration, baselines, repeat runs and checks that make results comparable and failures diagnosable.

How would you evaluate whether an algorithm improves controllability without sacrificing learning efficiency?

What to coverThe role asks for algorithms that improve both controllability and learning efficiency in large AI systems. Suggested preparation: define observable measures for each, propose controlled comparisons, and explain how you would report a gain on one measure that comes at the expense of the other.

What would you test when developing a foundation model for structured or tabular data?

What to coverFoundation models for structured and tabular domains are listed among the lab's research areas, though not as a specific assigned project. Suggested preparation: discuss relevant baselines, transfer across datasets and evaluation risks in high-stakes applications without assuming a particular dataset or deployment.

Behavioural practice

Tell me about a research question you pursued independently from idea to evidence.

What to coverIndependent research is part of the stated ideal qualifications. Suggested preparation: use a real situation, describe the decisions and experiments you personally drove, and state the result and remaining uncertainty.

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

What to coverThe role involves designing and evaluating research algorithms. Suggested preparation: give a real situation, the diagnostic steps and changes you took, and the resulting conclusion rather than presenting an inconclusive result as a success.

Give an example of collaborating across different research perspectives on a difficult problem.

What to coverThe posting describes collaborative research and global collaboration. Suggested preparation: identify a real disagreement or coordination challenge, your actions to resolve it, and the effect on the work; do not assume a particular team structure.

Tell me about a time you made a research result or tool usable by others.

What to coverPublishing and contributing to open-source research tooling are stated responsibilities. Suggested preparation: describe the audience, your contribution to clarity or reproducibility, and a concrete result or lesson learned.

Describe a time you weighed model performance against a safety or interpretability concern.

What to coverThe role aims to advance safe, transparent and controllable AI systems. Suggested preparation: use a real situation to explain the competing considerations, what evidence informed your action, and the outcome or unresolved trade-off.

Sharpen your knowledge

Topics to prepare

Multi-agent reinforcement learning and exploration
Revision suggestion: Derive how exploration and credit assignment affect coordination in a multi-agent setting, then propose measures of learning efficiency and coordination. The posting explicitly calls for research on multi-agent RL, agent exploration and algorithm evaluation.
Continual learning and catastrophic forgetting
Revision suggestion: Explain why sequential learning can erase earlier capabilities and sketch an experiment that measures retention alongside adaptation. The posting identifies continual learning and adaptive agents that avoid catastrophic forgetting.
Alignment and behavioral control
Revision suggestion: Compare reward modeling, safety fine-tuning, pruning, unlearning and training-time control by the behavior each could change and the evidence needed to assess controllability. All are named areas of investigation in the posting.
Mechanistic interpretability
Revision suggestion: Walk through how representation analysis and circuit discovery could support a claim about a model’s internal behavior, including a possible alternative explanation. The role explicitly includes model transparency and causal reasoning tools.
Information-theoretic and causal analysis
Revision suggestion: Explain what mutual information can reveal about internal representations, what it cannot establish causally, and how an intervention might test a claim about a model’s decision. The posting names both approaches for studying knowledge and decisions.

Skills to revise

Python model-training experimentation
Practice suggestion: In Python and one of the posting’s example frameworks—PyTorch, JAX or TensorFlow—run a small model-training comparison, record settings and summarize variability across runs. The qualifications explicitly seek hands-on model training and strong Python experimentation skills.
RL experiment design and evaluation
Practice suggestion: Specify a baseline, a proposed algorithm change, an ablation and metrics for learning efficiency, coordination and controllability; describe how to make the comparison reproducible. The role involves building RL experimental frameworks and evaluating new algorithms.
Testing claims about internal behavior
Practice suggestion: Design a representation-analysis experiment with a targeted intervention and a control condition, then state what result would weaken the proposed explanation. This directly reflects the responsibility to analyze model internals using representation analysis and causal reasoning.
Research synthesis and presentation
Practice suggestion: Prepare a concise account of one research question, its method, strongest evidence, limitations and next experiment. The posting seeks independent research and publication ability; this is interview preparation, not an additional employer requirement.

Your preparation checklist

Choose a research direction
Prepare a short statement of the area you would pursue over the next 1–2 years and why it fits the role’s focus on agent learning, alignment or interpretability. The application asks for a preferred research area and topic.
Rehearse a research project walkthrough
Prepare one example covering the problem, your contribution, methods, baselines, results, limitations and next experiment. The application specifically asks about a problem solved in your current role that you are proud of.
Work through an algorithm design exercise
As interview preparation, sketch an approach to multi-agent coordination or continual learning. Specify the objective, comparison baseline and measures of learning efficiency, coordination or forgetting; these reflect problems named in the posting.
Plan an interpretability or alignment experiment
Outline a testable question, intervention and evaluation method—for example, using a causal intervention to probe a model representation. Explain what result would challenge your hypothesis. This is a suggested exercise, not a stated interview requirement.
Gather evidence of experimental practice
Identify examples of model training, Python research tooling and reproducible evaluation using PyTorch, JAX or TensorFlow where applicable. Be ready to explain implementation choices and how you checked results.
Organize research and application materials
Prepare a publication or research-output list with your specific contributions, plus accurate answers to the application’s questions about A* papers, datasets, preferred topic, location and work experience. Do not imply a publication count the posting does not specify.
Draft questions for the research team
Ask how AryaXAI and Lexsi Labs prioritize research directions, evaluate safety and controllability claims, and balance open-source tools with publications. Clarify the working location if needed: the role header says Bangalore hybrid, while the application offers Mumbai or Bangalore.

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

KEEP EXPLORING

Similar AI jobs

Related skills and specializations in India. Matched to this role, not your profile.

Explore more jobs

Finding similar opportunities…