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Machine Learning Engineer, Applied AI - Deployed

Brain Co.· Engineering
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Hybrid (Abu Dhabi), Hybrid (Doha)Full-timePosted 11mo ago

About the role

Machine Learning Engineer role focused on building production-ready, end-to-end AI systems for institutional applying AI across regulated industries, with emphasis on composite AI, agentic systems, and production-grade evaluation.

Responsibilities

  • Turn ambiguity into shipped systems — from no problem statement, no labeled data, and no agreed definition of success, to well-posed ML problems and production deployments.
  • Own AI systems end-to-end. There is no handoff: the person who trains the model owns its behavior in production.
  • Work at the research frontier with production stakes, applying LLMs, RL fine-tuning, and agentic systems where the output is a decision an institution acts on.
  • Work directly with the institutions we serve — permit reviewers, underwriters, compliance officers — to understand how decisions actually get made and ensure your systems change how the work gets done.
  • Engineer for production reality, navigating accuracy, latency, cost, and reliability in environments far messier than any benchmark.
  • Raise the bar across the company through design reviews, our internal paper club, and the shared playbook for AI systems institutions can trust.

Qualifications

  • You understand how machine learning actually works — not just the tooling, but the philosophy underneath: what a loss function really optimizes, how generalization breaks under distribution shift, why evaluation is where systems quietly go wrong.
  • You live at the bleeding edge of modern AI, with hard-won instincts for squeezing the most out of LLMs and agentic systems — prompting, fine-tuning, tool use, and reasoning.
  • You know when a fine-tuned segmentation model beats a VLM, when a rule engine beats both, and how to compose all three into a system more accurate than any single model.
  • You treat frontier models as components to be measured, pushed, and engineered — never as magic.
  • Most of all, you're energized by building things that have never existed, and comfortable when the problem, the data, and the definition of success all have to be invented at once.

Skills

Required:End-to-end AI system ownershipLLMs and agentic systemsProduction-grade evaluation
Last updated Sep 24, 2026. Interested? Apply on the company site.

About Brain Co.

AI for the world's most important institutions

Building an AI platform and applications to deliver impact for real-world problems.

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