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Mimica is an AI-powered process intelligence platform that automatically analyzes enterprise work and generates AI agents to execute it. The company has raised $26M in Series B funding and is backed by Khosla Ventures and Paladin Capital, serving Fortune 500 companies.
You'll join the Data Intelligence team as a Senior Machine Learning Engineer, owning key parts of ML pipelines end-to-end. Your focus will be on making pipelines more accurate, cost-efficient, and trustworthy through applied LLM work, evaluations, and production improvements.
Day-to-day responsibilities include:
- Owning, debugging, and improving production pipelines for accuracy and efficiency
- Designing and building LLM evaluation systems, including datasets, metrics, and benchmarks
- Running rigorous experiments on prompts, model choice, pipeline design, and inputs
- Driving LLM efficiency improvements by comparing models and replacing LLM calls with smaller, purpose-built models
- Expanding anonymization pipelines and evaluating existing models
- Collaborating with Python and quality engineers to move experiments into production
- Documenting findings in experiment reports and design docs
The team is remote-first and globally distributed across 16+ countries, with a culture emphasizing high ownership, bottom-up thinking, and transparent communication. You'll work in small, project-based teams with flexibility in problem selection and full lifecycle ownership.
REQUIREMENTS:
- Solid machine learning/data science background with understanding of experimental design, data splits, metrics, bias, and statistical significance
- Hands-on experience applying LLMs in production or near-production settings, especially evaluating them (building eval sets, LLM judges, benchmarks, iterating on prompts and pipelines)
- Comfortable exploring messy data, troubleshooting issues, and transforming insights into measurable improvements
- Strong Python skills and good engineering practices; ability to write clear, tested code suitable for production
- Ability to work independently on ambiguous problems: framing questions, choosing approaches, and setting acceptance criteria
- Proficiency using AI coding tools while maintaining accountability for output
- Clear communication in spoken and written English, including explaining technical trade-offs to diverse audiences
BONUS/NICE TO HAVE:
- Experience with classical ML or deep learning model development
- Experience with image/OCR pipelines or face-related anonymization
- Experience shipping end-to-end ML features in startup or scale-up environments
