ML Research Scientist

About Think

We make the world’s compute radically more efficient.

Think AI was founded in Riyadh in 2025 on one problem: expensive silicon sitting underused, held back by software blind to the hardware it runs on. We design and build the machine and the orchestration together, in one building, so that stops being true.

Our Mission

To redefine AI infrastructure from the silicon up.

To build the most efficient compute platform on earth, where hardware and software co-evolve to maximise intelligence. To ignite a new golden age of engineering, where anyone can own, operate, and scale frontier AI without compromise, without dependency, and without wasted compute.

Our Values

Four principles run through everything we do. We look for these in how people demonstrate the values in their everyday work, not in how they talk about themselves.

Ownership مسؤولية

You own the outcome, not just the task. You’re frugal with resources and you put the mission ahead of your own comfort.

Agility مرونة

You move with velocity. You ship quickly and reiterate and once a decision is made, you disagree and commit.

Impact أثر

You build for the customer. You think big and execute simply and you measure yourself on outcomes, not activity.

Mastery إتقان

You go deep on the hard problem. Excellence is your default and you take real pride in delighting the customer.

The Role

Ask the questions whose answers change what the platform does. The work is empirical and it runs on our own hardware: measure what really happens when models share silicon, and turn that into something the product can use.

What you will do

• Design and run experiments on real fleets rather than on synthetic proxies.


• Investigate model behaviour under co-location, quantisation, adapter serving and mixed-precision execution.


• Turn findings into decisions the orchestration layer can act on, and work with engineering to land them.


• Publish internally with the rigour you would use externally: method, data, limits.


• Keep the team honest about what a result does and does not support.


• Track the literature and separate what is real from what is claimed.

What you need

• A doctorate or equivalent research experience in machine learning, systems or a numerate discipline.


• You have run experiments end to end, including the unglamorous instrumentation.


• Strong Python and comfort reading and modifying model and serving code.


• The discipline to state a limitation before someone else finds it.


• Useful, not required


• Published work in efficiency, quantisation, serving or systems for machine learning.


• Experience with adapter methods, distillation or mixture-of-experts serving.


• A track record of research that shipped.

What we offer

• One of the first deep tech companies in the region, building foundational technology in house.


• Meaningful ownership and impact at an early stage.


• Competitive early-stage compensation.


• Close collaboration with a small, senior team.


• Problems that combine hardware, systems and AI at scale.