AI Engineer (NLP)

We're looking for a Senior ML/AI Engineer to own and evolve our LLM\-powered user experience. You'll work directly with our technical co\-founder to build, optimize, and monitor agent systems that parse workout descriptions, provide scaling recommendations, and enable conversational data retrieval \- all with production\-grade accuracy and speed. This is a hands\-on role focused on the ML/AI engineering side: prompt engineering, model optimization, agent orchestration, and continuous improvement based on real\-world usage patterns. **What You’ll Do** **Core Responsibilities** * Own the workout parsing system: improve accuracy of our fine\-tuned model (currently Qwen\-based) that converts natural language workout descriptions into structured schemas * Design and implement agent workflows for workout scaling recommendations and performance tracking * Build observability workflows using Langfuse to identify and systematically address model performance issues * Optimize agent response latency while maintaining accuracy across our tool\-based reasoning system * Collaborate on agent architecture decisions, including potential migration to frameworks like DSPy * Ship production features: workout entry system, scaling recommendations, and score reporting **What We’re Looking For** **Required** * 5\+ years of ML/AI engineering experience with at least 2 years working with LLMs in production * Strong prompt engineering and model optimization skills * Experience building and deploying agent systems with tools/functions * Proven ability to use observability platforms to diagnose and improve model performance * Experience with model fine\-tuning (any framework/approach) * Strong Python programming skills * **Active CrossFit participant** \- candidates should understand standard movements and workout structures **Strongly Preferred:** * Experience with agent orchestration frameworks (DSPy, LlamaIndex, or similar) * Background in production ML operations and monitoring * Experience with Modal.com or similar serverless ML platforms * Track record of iteratively improving LLM systems based on user feedback and metrics * Experience fine tuning similar open\-source LLMs **Success in First 6 Months** * Ship workout entry system with improved parsing accuracy * Launch basic workout scaling recommendations * Implement user score reporting and retrieval * Establish robust monitoring workflows to catch and address model failures and poor user experiences * Contribute to agent architecture decisions as we scale