ML Research Scientist
Role Overview
Conduct empirical research on the company’s hardware to measure what happens when models share silicon and turn findings into product decisions.
Key Skills for This Role
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About Think
Think AI was founded in Riyadh in 2025 to improve compute efficiency by designing hardware and orchestration software together.
The company’s mission is to build an efficient compute platform where hardware and software co-evolve to support AI.
Company values
Ownership: own outcomes, use resources carefully, and put the mission first.
Agility: move quickly, iterate, and commit after decisions are made.
Impact: build for customers, execute simply, and measure outcomes.
Mastery: pursue difficult problems with rigor and take pride in excellent work.
The role
Conduct empirical research on the company’s hardware to measure what happens when models share silicon and turn findings into product decisions.
Responsibilities
- Design and run experiments on real fleets rather than synthetic proxies.
- Investigate model behavior 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 implement them.
- Publish internally with rigorous methods, data, and limitations; communicate what results do and do not support.
- Track the research literature and distinguish demonstrated results from claims.
Requirements
- A doctorate or equivalent research experience in machine learning, systems, or a numerate discipline.
- Experience running experiments end to end, including instrumentation.
- Strong Python skills and comfort reading and modifying model and serving code.
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 the company offers
- Opportunity to help build foundational technology at an early-stage deep tech company in the region.
- Meaningful ownership and impact, close collaboration with a small senior team, and problems combining hardware, systems, and AI at scale.
Compensation
- Competitive early-stage compensation; no pay cadence or amount is specified.
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