*NO AGENCIES* We are building the intelligence layer and are looking for a strong applied ML/AI engineer who combines disciplined execution with proactive systems thinking. You will work directly with intelligence advisors and product to implement end-to-end solutions, refine, QA, and improve the intelligence layer powering personalization and insights. We are looking for someone who executes precisely, thinks critically, and proactively improves systems. You are: • A hands-on builder • A rigorous implementer • A proactive systems thinker • A collaborative operator • Someone who ships weekly and improves continuously Clarifying, executing, testing, and refining weekly. ⸻ Core Responsibilities Intelligence Implementation • Translate structured specs into clean, production-ready systems • Implement physiology-informed logic and edge-case handling • Ensure outputs are grounded, stable, and explainable • Maintain alignment with defined intelligence architecture ML & LLM Systems • Build hybrid ML + heuristic + LLM systems • Engineer features from time-series and behavioral data • Design structured LLM pipelines (prompting, retrieval, schemas) • Run evaluations and improve quality based on measurable results • Think about reliability, performance, and maintainability Proactive Improvement • Identify inefficiencies in current pipelines • Suggest improvements to logic or experimentation design • Flag inconsistencies early • Strengthen evaluation frameworks • Improve documentation and clarity across teams You execute AND elevate. ⸻ What We’re Looking For • Strong Python, data science, and applied ML experience • Experience building LLM-powered features in production • Comfortable implementing structured specs • Detail-oriented and rigorous • Strong cross-team communicator • Proactive without being disruptive • Comfortable working in fast weekly sprint cycles ⸻ Ideal Profile • 3+ years applied ML/AI experience, shipping end-to-end production solutions • Has worked under technical leadership while owning execution; knows how to anticipate and up-manage • Has improved systems they were implementing • Thinks in feedback loops and quality control • Enjoys building clean, stable systems • Wants long-term involvement in building something meaningful ⸻ Engagement • Full-time • 1-month paid trial. Long term placement then assessed for mutual fit • Direct collaboration with startup leadership ⸻ Required in Application 1. A production ML/LLM system you implemented. 2. An example of how you improved a system beyond initial spec. 3. How you validate correctness in your own work. 4. Confirmation you are applying as an individual.