Machine Learning Engineer, Applied AI - Deployed

Brain Co.
Abu Dhabi, Doha
Workplace: HybridFull timeFunction: Data Science & Machine LearningSkills: ["End-to-end ownership","Evaluation-focused mindset","Systems thinking","Problem solving","Stakeholder collaboration"]

Build deployed, production-grade Applied AI systems that turn ambiguous institutional problems into decisions that regulators and operators can trust. You’ll design and train models for complex, multimodal documents; develop composite AI systems with vision transformers, VLM reasoning, and rule engines; and own systems end-to-end—from problem framing and data/evals to reliability, latency, cost, and measurable customer impact.

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FursaFursa
Brain Co.
Brain Co.
1 year ago

Machine Learning Engineer, Applied AI - Deployed

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Source: Company careers pageValidated by: Fursa AI
Last checked: 4 hours agoStatus: Live

Job Summary

Build deployed, production-grade Applied AI systems that turn ambiguous institutional problems into decisions that regulators and operators can trust. You’ll design and train models for complex, multimodal documents; develop composite AI systems with vision transformers, VLM reasoning, and rule engines; and own systems end-to-end—from problem framing and data/evals to reliability, latency, cost, and measurable customer impact.
Location: Abu Dhabi, Doha
Workplace: Hybrid
Employment Type: Full time
Job Function: Data Science & Machine Learning

Key Responsibilities

  • •Turn ambiguous customer problems into well-posed ML problems and production deployments.
  • •Own AI systems end-to-end, with no handoff from training to production behavior.
  • •Apply LLMs, RL fine-tuning, and agentic systems where outputs become decisions institutions act on.
  • •Collaborate with the institutions served (e.g., permit reviewers, underwriters, compliance officers) to understand how decisions are made.
  • •Engineer for production reality across accuracy, latency, cost, and reliability; raise the bar through design reviews and shared practices.

Key Requirements

  • •Understand how machine learning works, including loss functions, generalization under distribution shift, and why evaluation is critical.
  • •Build and engineer frontier AI systems using LLMs and agentic systems (prompting, fine-tuning, tool use, reasoning).
  • •Know when different model types (e.g., fine-tuned segmentation models vs VLMs) and rule engines outperform, and how to compose them into higher-accuracy systems.
  • •Treat frontier models as measurable components—designed, evaluated, and engineered—not “magic.”
  • •Be comfortable with situations where the problem, data, and definition of success must be invented together.
Skills:End-to-end ownershipEvaluation-focused mindsetSystems thinkingProblem solvingStakeholder collaboration
Tech Stack:Machine learningLLMsAgentic systemsPromptingFine-tuningTool useReasoningVision transformersSegmentation modelsVLM reasoningRule enginesRL fine-tuningComposite AI systems

Company Brief

Brain Co.
Brain Co. builds agent-native operating systems for regulated institutions across government, health, insurance, finance, and enterprise. Its platform emphasizes secure deployment, workflow integration, and AI applications tailored to complex operational environments.
Industry: AI & Machine Learning
Growth: Growth Stage Startup
Headquarters: San Francisco, United States
Founded: 2026
WebsiteLinkedIn