ML Research Scientist

Job Summary

Conduct empirical research on machine learning models running on shared hardware infrastructure. Design and execute experiments to understand model behavior and translate findings into actionable insights for the platform’s orchestration layer.

Responsibilities

• Design and run experiments on real hardware fleets rather than synthetic proxies.
• Investigate model behavior under co-location, quantisation, adapter serving and mixed-precision execution.
• Convert research findings into decisions that the orchestration layer can implement, working with engineering teams to deploy them.
• Publish internally with the same rigor applied to external publication: documenting method, data and limitations.
• Ensure the team maintains accuracy about what results do and do not support.
• Monitor machine learning literature and distinguish between validated findings and unsubstantiated claims.

Must haves

• Doctorate or equivalent research experience in machine learning, systems or a numerate discipline.
• End-to-end experimental execution experience, including instrumentation work.
• Strong Python proficiency and ability to read and modify model and serving code.
• Discipline to identify and state limitations before others discover them.

Nice to haves

• Published work in efficiency, quantisation, serving or systems for machine learning.
• Experience with adapter methods, distillation or mixture-of-experts serving.
• Track record of research that shipped to production.

What the company offers

• Position at 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 combining hardware, systems and AI at scale.

We refresh listings regularly, but some roles close early on the source platform.