Member of Technical Staff - Models & Learning

Every product ever built is designed to keep you in the loop.WYLE is built to take you out of it.We’re building Buddy: a personal intelligence that learns one specific person deeply enough to increasingly think, decide and act the way they would at their best, while remaining bounded by their own values.Not a chatbot.Not a copilot waiting to be prompted.The end state is something closer to a persistent extension of the person themselves.I’m looking for someone who wants to work on the intelligence itself.The ProblemCurrent AI systems are remarkably capable. But what we are trying to build requires more than putting a frontier model behind tools.Buddy has to live alongside one person for years, learn from what happens, and become more capable through that experience.That creates problems I do not think the field has solved.How should an intelligence learn continuously from years of decisions, actions, corrections and outcomes without becoming unstable or forgetting what it already knows?When should an experience become memory, a preference, a learned skill, a policy change, a model update, or nothing at all?How do you teach judgment from longitudinal real-world outcomes rather than only static preference data?How does an agent learn whether an action worked for the right reason, assign credit when consequences arrive much later, and improve how it reasons and acts the next time?We will use existing methods wherever they are good enough.Open-weight models. Fine-tuning. Post-training. Reinforcement learning. Preference optimisation. Verifiers. Distillation. Test-time compute. Continual learning. Agent learning.But I do not assume today’s methods are sufficient.Where they are not, I want us to find out why and build something better.That is the job.What you would work onYour primary technical domain is Models & Learning.That includes model training and post-training, reinforcement and preference learning, continual and online learning, reasoning, agent learning, model adaptation, evaluation, reward models and verifiers, synthetic environments, inference-time learning, specialist models, and new learning methods where existing approaches are insufficient.This is deliberately not a fixed research roadmap.If the most important thing we are working on eighteen months from now does not have a name today, that is a good outcome.You will also build.An idea here has to survive the path from hypothesis to experiment to code to training run to evaluation to a working system.I am not looking for a structure where one person researches and somebody else turns it into reality.Who I am looking forI care far less about your current title, age, or number of years worked than I do about evidence of unusual technical ability.You may come from a research lab. You may have a PhD. You may not.You may be a research engineer whose strongest work never became a paper.You may have built important open-source systems or models.You may simply have spent the last few years going unusually deep on hard problems.What matters is that you can go beyond implementing what already exists.You understand modern machine learning deeply enough to question its assumptions.You can read research, reproduce it, break it, modify it and design the experiment that tells you whether your alternative is actually better.You have research taste: you can distinguish something technically interesting from something that actually matters.You can reason from first principles when there is no established playbook.You are as comfortable opening a codebase as opening a paper.You do not need a fully specified ticket before you can start thinking.And you are willing to tell me when an idea is technically wrong, including mine, and do the work required to prove it.Who this isn't forThis is not an applied-AI integration role.If your experience is primarily prompting models, calling APIs, assembling RAG pipelines and wiring together agent frameworks, this is probably not the right role.It is also not a purely academic research position.If the work ends for you when the experiment or paper is finished and somebody else needs to make the system work, this is probably not the right place either.This is not a management-first role. There is no large research organisation waiting for you to direct.And this is not a co-founder position. It is a full-time technical role with meaningful scope, autonomy and equity.I care much more about whether you want the problem than whether you want the title.The ambitionI do not want WYLE to become a thin application layer around somebody else’s intelligence.Over time, I want us to create technology that is genuinely ours and contribute new ideas to the field where the problems demand it.The product forces us toward difficult questions.How do machines learn continuously?How do they acquire judgment?How do they learn from consequences rather than static examples?How should persistent agents improve from experience?How do reasoning, memory and learning interact over years rather than individual sessions?How do we build systems that become more capable without losing alignment with the person they serve?If we do this properly, some of the technology Buddy eventually needs may not exist yet.That is part of the attraction.Location & PackageFull-time and on-site in Dubai.This is not a remote role.Compensation and equity are discussed early in the process with shortlisted candidates.How to applyPlease do not send only a CV.Send me four things:Something you built, discovered or materially changed that was not obvious when you started. What was the underlying insight, what did you personally do, and what evidence eventually convinced you that you were right or wrong?One important idea in modern AI that you think the field currently misunderstands, overestimates or underestimates. I care more about the reasoning than whether I agree.If you had three years, enough compute and the freedom to work on one unsolved problem in machine intelligence, what would you choose and why?Something technical I can inspect that you personally contributed to. Code, a model, a paper, an experiment, open-source work or technical writing. Be clear about which parts were yours.I am looking for evidence of how you think and what you can build, not how well your CV has been formatted.Send it to: ceo@wyle.ai