ML Engineer (AI-Native Systems & Forecasting)

Ando Technologies, Inc•San Francisco, CA

2d•Onsite

About The Position

About Ando Ando is building AI-native workforce infrastructure for the 80M hourly workers in the U.S. and hundreds of millions globally. Labor is the last broken supply chain. Forecasting, allocation, and professional identity remain fragmented across static tools and manual decision-making. Ando is rebuilding this system from first principles. We start with highly accurate AI-generated demand forecasts, move to allocation-first labor optimization, and compound value through a persistent labor graph that captures skills, reliability, availability, and performance across time and employers. Over time, this becomes a professional identity layer and intelligent system serving both enterprises and frontline workers. Ando is a Seed-stage company backed by Slow Ventures and experienced operators. We are live with real customers and entering a phase where architectural clarity, learning loops, and AI systems quality are foundational to long-term success. ML Engineer (AI-Native Systems & Forecasting) Location: San Francisco, CA The Role You will own the design, development, and production deployment of Ando’s machine learning systems, including demand forecasting, labor allocation intelligence, and LLM-powered workflows. This is a production ML role. You will work across the full data and ML lifecycle - from ingesting inconsistent real-world data to building reliable, continuously improving systems in production. You will operate with high autonomy, make pragmatic modeling decisions, and build systems that directly impact real-world outcomes for businesses and workers. This role is designed as a foundational technical leadership position, with meaningful influence over data architecture, model strategy, and system reliability.

Requirements

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

Nice To Haves

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

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