Note: The job is a remote job and is open to candidates in USA. Ando is building AI-native workforce infrastructure for hourly workers, focusing on creating accurate demand forecasts and optimizing labor allocation. The ML Engineer will design, develop, and deploy machine learning systems, impacting real-world outcomes through the full data and ML lifecycle.

Responsibilities

• Design, build, and deploy production-grade ML systems for demand forecasting and labor optimization
• Own the full ML lifecycle, including data ingestion, feature engineering, model training, deployment, and monitoring
• Inherit and remediate messy, inconsistent datasets and establish scalable data pipelines
• Architect data systems across ingestion, warehousing, transformation, and feature stores
• Build and maintain LLM-native systems, including RAG pipelines, prompt systems, and evaluation frameworks
• Make pragmatic decisions on modeling approaches, including when to use APIs, fine-tuning, or custom models
• Design and implement model evaluation systems that measure performance continuously, not just at launch
• Implement monitoring, drift detection, and feedback loops to improve model performance over time
• Design and run experiments, including A/B testing and statistical validation of model performance
• Translate model performance and tradeoffs into clear insights for product and business stakeholders
• Collaborate closely with Product, Engineering, and Operations to integrate ML into core workflows

Skills

• 5–10+ years of experience in machine learning, data science, or applied AI roles
• Proven experience shipping ML systems into production environments
• Strong experience working with real-world, imperfect datasets in mid-maturity or scaling organizations
• Deep understanding of the full data stack, including ingestion, warehousing, feature engineering, and model serving
• Experience designing and operating ML pipelines and workflows in production
• Hands-on experience with LLM systems, including RAG, prompt design, and evaluation frameworks
• Strong foundation in statistics, experimentation, and model evaluation
• Experience with monitoring, observability, and model performance tracking over time
• Ability to operate with high ownership, ambiguity, and minimal process overhead
• Strong communication skills, with the ability to translate technical decisions into business impact
• Experience with time-series forecasting, demand modeling, or optimization systems
• Experience building or integrating with labor, logistics, or marketplace systems
• Familiarity with modern ML infrastructure (Airflow, dbt, feature stores, etc.)
• Experience fine-tuning or training custom models
• Experience hiring or mentoring ML or data team members

Company Overview