Machine Learning Engineer - Applied AI Deployed Systems

Our Mission

We are committed to transforming global institutions, making them more effective for the communities they serve.

About Brain Co.

Brain Co. develops AI-powered operating systems tailored to large, highly-regulated organizations. Each system is customized for its industry, leveraging autonomous agents that advance actual workflows. At the core lies Atlas — our exclusive platform designed for customer control, security by design, and model flexibility.

Why Join Us Now

As we scale national production deployments, our team, comprised of veterans from top technology firms, grows rapidly across sectors like government, insurance, healthcare, and finance. Joining us at this phase means contributing to building a pioneering category in applied AI, with every project directly impacting customers and moving beyond prototypes.

About The Role

Many critical societal processes remain inefficient, with long delays in permit approvals and insurance claims. AI hasn’t fully resolved these because the challenge lies beyond models — it’s the complex institutional context encompassing rules, records, relationships, and legacy systems. Brain Co. addresses this by creating agent-native operating systems that handle these complexities — the first worldwide to fully automate construction permitting and now expanding into insurance and more.

As a Machine Learning Engineer focusing on Applied AI, your main task is operationalizing impressive models into dependable production-grade decision systems used by institutions day-to-day. Challenges vary from building custom vision pipelines verifying building blueprints with over 95% accuracy, agents clarifying insurance policy details, to predictive systems analyzing clinical data to assess patient care trajectories. You will lead projects from ambiguous requirements through comprehensive evaluation to deployment, ensuring error classes are addressed.

This role tackles frontier machine learning in challenging real-world settings, demanding institutional-level precision, the ability to handle complex, multimodal documents, and delivering systems that affect meaningful workflows daily.

Candidate Profile

We seek individuals who deeply grasp machine learning fundamentals—not just tools but the underlying principles such as loss function optimization, handling distribution shifts, and thorough evaluation. Expertise in state-of-the-art AI, including large language models (LLMs) and agentic systems, is essential, with strong skills in prompt engineering, fine-tuning, tool integration, and reasoning. You recognize when to prefer specialized segmentation models, vision-language models, or rule-based engines, and can combine these into superior composite systems. Frontier AI models are components to be crafted, measured, and enhanced—not treated as magic.

The ideal candidate thrives in ambiguity, eager to create novel solutions where the problem, data, and success metrics must be defined collaboratively and iteratively.

Key Challenges

• Designing composite AI pipelines combining vision transformers, segmentation, vision-language reasoning, and rule systems while accurately tracing failure to the correct component.
• Advancing document understanding for complex, multi-modal materials such as blueprints, policies, contracts, and clinical records that challenge off-the-shelf models.
• Developing agents that learn from real, verified outcomes generated by live deployments, crafting data collection strategies, evaluation frameworks, and training regimens.
• Establishing rigorous evaluation methodologies vital for institutional trust and regulatory approval.
• Building systems that improve continuously by incorporating verified corrections, enhancing both the stored knowledge and the AI’s capabilities.

Responsibilities

• Transform vague and undefined problems into well-defined machine learning solutions ready for production deployment.
• Take full ownership of AI systems, from training to production behavior, with no handoffs.
• Engage in cutting-edge AI research with direct production impact, utilizing techniques such as LLMs, reinforcement learning fine-tuning, and agentic AI.
• Collaborate closely with end users like permit reviewers and compliance officers to ensure system effectiveness and practical impact on workflows.
• Design solutions that balance accuracy, latency, cost, and system reliability within complex operational environments beyond standard benchmarks.
• Contribute to quality and knowledge sharing through design reviews, internal study groups, and developing a company-wide AI systems playbook.

Additional Information

Role Level: Not Applicable
Work Type: Full-Time
Country: United Arab Emirates
City: Abu Dhabi
Industry Sector: Software Development
Company Website: brain.co

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