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Machine Learning Engineer

TigerEye

TigerEye

Software Engineering
United States · Remote
Posted on May 23, 2025

TigerEye is full stack, modern business intelligence built for go-to-market teams. We automate marketing, sales and finance reporting with live dashboards and conversational AI to deliver instant, accurate answers to complex questions about your business.

Here are a few of the things that you might do as AI/ML Engineer at TigerEye:

  • Design, develop, and validate statistical models to explain past behavior and to predict future behavior of our customers’ go-to-market teams

  • Own training, integration, deployment, versioning, and monitoring of ML components

  • Improve TigerEye’s existing metrics collection and analysis techniques in order to expand the range of questions TigerEye is able to explore

  • Build a customizable heuristic system to surface actionable insights

  • Provide quantitative rationale to inform group decision-making processes

  • Design and build our ML infrastructure, including integration with customer data

  • Develop AI agents (both standalone agents, as well as within the context of multi-agent architecture) from start to finish. This includes from product ideation, to agent/LLM orchestration to qualitative and quantitative evaluation

Qualifications

Minimum

  • A Graduate degree in Statistics, Applied Mathematics, or a related field/equivalent experience

  • Substantial experience with statistics and data modeling

  • Knowledge of statistical techniques to build predictive models

  • Knowledge of time series analysis

  • Solid understanding of computer science, coding principles and algorithms

  • Proficiency in programming languages such as Go, Dart, and Python

Preferred++

  • Proficiency in SQL

  • Experience with the full lifecycle of building a ML-powered product

  • Experience building ML infrastructure

  • Experience with data engineering and building an ETL pipeline

  • Experience with cloud computing services (AWS, GCP, etc.)

  • Experience working with LLMs