Machine Learning Engineer, Applied AI - Production Deployment

Our Mission

We aim to fundamentally redesign institutional operations to enhance their effectiveness and responsiveness to the people they serve.

About Brain Co.

Brain Co. develops AI-native operational systems tailored for large, regulated organizations across various sectors. Each solution is industry-specific and powered by intelligent agents that advance real workflows. At the core is Atlas, our proprietary platform ensuring customer control, security by design, and eliminating vendor lock-in.

Why Join Us Now

Brain Co. is scaling production deployments nationally, guided by a distinguished team from Palantir, Google, Meta, and Nvidia, expanding influence in government, insurance, healthcare, and finance. Joining now offers the unique opportunity to shape the future of applied AI and directly impact societal institutions with solutions that move to production immediately and generate measurable outcomes.

About the Role

Society faces delays and inefficiencies in essential processes like permitting and claims resolution. Current AI efforts have yet to transform these areas because the challenge lies not in the models themselves but in understanding institutional contexts—rules, histories, relationships, and judgments scattered across people and legacy systems.

Brain Co. is addressing these challenges by building agent-native operating systems that have pioneered full automation of construction permits and are extending this innovation to insurance and other critical industries. This role requires ownership from vague problem definition through evaluation and deployment of cutting-edge ML systems that support institutional decision-making with reliability and high accuracy.

Who We're Looking For

We seek candidates with a deep understanding of machine learning fundamentals—not just tools, but the underlying principles such as optimization objectives, distribution shifts, and rigorous evaluation. Experience with advanced AI models including LLMs and agentic systems is essential, along with the ability to judiciously combine techniques like fine-tuned segmentation, vision language models, and rules to form superior composite systems. You should be passionate about pioneering novel solutions where data, problem framing, and success metrics must be established from scratch.

Problems You Will Solve

• Enhance complex AI pipelines involving vision transformers, segmentation, reasoning, and rule engines by diagnosing component failures.
• Develop advanced document understanding for challenging multimodal inputs such as blueprints, site plans, insurance policies, contracts, and clinical records that outperform standard models.
• Design learning and evaluation loops that leverage real outcomes from deployed agents to continuously improve system performance.
• Create evaluation frameworks rigorous enough to gain regulatory approval and build institutional trust.
• Build adaptive institutional intelligence that benefits from verified corrections to simultaneously improve multiple applications and the system itself.

Your Responsibilities

• Translate ambiguous inputs into fully deployed machine learning systems, defining problems, gathering data, and setting success criteria independently.
• Take full ownership of AI systems from training to production behavior without handoffs.
• Operate at the research frontier with real-world production stakes, applying techniques such as large language models, reinforcement learning fine-tuning, and agent-based systems toward actionable institutional decisions.
• Collaborate closely with end-users like permit reviewers, underwriters, and compliance officers to ensure AI systems positively transform their workflows.
• Engineer production-ready solutions balancing accuracy, latency, cost, and robustness within complex, noisy environments.
• Contribute to elevating company standards through design reviews, internal knowledge sharing, and developing reliable AI playbooks for regulated institutions.