Machine Learning Engineer

About the Company

Kepler is building the agent harness—the infrastructure layer that makes AI outputs reliable, traceable, and verifiable. We provide a trust layer for high-stakes industries like finance, ensuring every AI-generated calculation and data point is auditable and reproducible.

Responsibilities

• Own models within Kepler's AI research platform, including model selection, fine-tuning, and evaluation
• Build evaluation harnesses to score agent research runs end-to-end
• Redesign model routing across workflows to optimize for accuracy, cost, and latency
• Systematically improve workflow success rates through better tools, verification rules, or fine-tuning
• Own systems end-to-end from research to production deployment

Requirements

• 5+ years of experience building production software
• Proven track record of shipping ML systems to production (fine-tuning, agents, retrieval, or structured extraction)
• Strong engineering fundamentals and experience with production-grade software
• Experience building measurement and evaluation frameworks for ML models
• Ability to work in a fast-paced, early-stage startup environment

Preferred Qualifications

• Experience with Rust
• Background in ML infrastructure or product-focused ML engineering

Benefits

• 100% covered medical, dental, and vision insurance for employees and families
• Company-maxed HSA
• Life, AD&D, and disability insurance
• Daily lunch in office
• Unlimited PTO
• Development environment budget (MacBook Pro, monitors, etc.)
• "Build anything" budget for tools and datasets
• Learning budget for conferences and courses