ML Research Scientist
think
Date: 2 weeks ago
City: Riyadh
Contract type: Full time
Open roles
ML Research Scientist
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.
Apply for this role See every open role
Type Full time
Team Software
Location Riyadh
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
ML Research Scientist
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.
Apply for this role See every open role
Type Full time
Team Software
Location Riyadh
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.
- 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.
- 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.
- 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.
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