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