ML Engineer (AI-Native Systems & Forecasting)

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. You Will 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 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 Have 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 Culture at Ando Ando is a high-trust, high-care organization built on ownership, respect, and thoughtful collaboration. We value diverse perspectives, clear communication, and personal accountability. We move fast through alignment rather than force, operate comfortably in ambiguity, and build with empathy for the enterprises and workers our systems serve. These values are core to how we work. Candidates who share them tend to thrive at Ando. Equal Employment Opportunity, DEI & Legal Notice Ando Technologies, Inc. is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender identity or expression, sexual orientation, national origin, genetics, disability, age, veteran status, or any other legally protected status. And yes, we are proudly DEI. We believe diverse perspectives make teams stronger, systems smarter, and outcomes more equitable. Building infrastructure for the frontline workforce demands empathy, representation, and respect, and we take that responsibility seriously. These values are core to how we work. Candidates who share them tend to thrive at Ando. By applying for this role, you acknowledge that any information you provide will be processed in accordance with Ando’s privacy practices and applicable data protection laws. Applicant information is used solely for recruiting and employment-related purposes. Submission of an application does not create an employment contract or guarantee of employment.

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