Research Scientist - Agents

AI in this role

pytorch

ai-agentsfine-tuning

About the Institute of Foundation Models We are a dedicated research lab for building, understanding, using, and risk-managing foundation models. Our mandate is to advance research, nurture the next generation of AI builders, and drive transformative contributions to a knowledge-driven economy. As part of our team, you’ll have the opportunity to work on the core of cutting-edge foundation model training, alongside world-class researchers, data scientists, and engineers, tackling the most fundamental and impactful challenges in AI development. You will participate in the development of groundbreaking AI solutions that have the potential to reshape entire industries. Strategic and innovative problem-solving skills will be instrumental in establishing MBZUAI as a global hub for high-performance computing in deep learning, driving impactful discoveries that inspire the next generation of AI pioneers. Position Summary As a member of the Agents team, you will tackle research and engineering challenges to train advanced agentic language models that are adept at using reasoning and tool use to complete tasks on a computer. These challenges range from building high-quality training datasets and robust evaluations to innovative reinforcement learning infrastructure, algorithms and environments. The team aims to recruit candidates across the research-engineering spectrum, from algorithmic and empirical research to large-scale systems and optimization, reflecting the breadth of interesting problems we aim to solve. We value depth in any subset of these areas; there is no single “right” background. Key Responsibilities · Propose, prototype, and scale algorithms/systems to support agentic learning · Develop new paradigms for agentic behavior that go beyond current practices · Contribute to technical reports, research publications, and open-source software · Collaborate with other teams to produce state-of-the-art foundation models Academic Qualifications • BS, MS, or PhD degree (or equivalent experience) in Computer Science, Machine Learning, or related fields Professional Experience Minimum · 2 years of experience working in one of the following or related areas: LLM training/fine-tuning, evaluations, reinforcement learning, LLM inference, distributed machine learning systems · Python and PyTorch development experience · Experience in using LLM agents for your personal or professional use · Experience in designing and implementing algorithms from scratch (for algorithms focus) · Experience in dataset curation and generation (for data focus) · Experience in building or contributing to training/serving infrastructure (for systems focus) · Experience in designing and deep diving into evaluations (for evals focus) Preferred Skills · Expertise in one or more of PyTorch, Ray, Triton, CUDA C++ · Strong knowledge of literature on RL, LLM reasoning, and tool use · Experience in training or using LLM agents for SWE tasks · Deep understanding of reinforcement learning principles · Experience implementing distributed learning algorithms · Contributions to published research and/or open-source ML software

How we rate this

Research Scientist - Agents at IFM rates 100 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

AI AgentsFine TuningPyTorch

Questions you could be asked

How do you decide when an AI agent can act on its own versus asking for approval first?
Walk me through fine-tuning a model: what data did you use, and how did you check the result?
What are the limits of PyTorch that you've run into, and how did you work around them?
How would you decide a model or AI system is ready to ship?
Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

List these exact terms on your resume: AI Agents, Fine Tuning, and PyTorch. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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