Principal Machine Learning Engineer- HelloData

Grace Hill

1d•$175,000 - $250,000•Remote

About The Position

Grace Hill is looking for a Principal Machine Learning Engineer to support our HelloData product, an automated multifamily market analysis platform that leverages daily data updates from hundreds of thousands of property websites and listing sites to drive apartment pricing and investment decisions. If you’re passionate about changing the game in multifamily real estate and thrive in a high-growth, high-reward environment, we invite you to be a part of our journey! Position Overview Every new HelloData feature begins with a data strategy. As part of the Data Engineering team the Principal Machine Learning Engineer will sit at the intersection of data science and production engineering. You aren’t just a researcher; you are a builder who knows how to take a sophisticated ML model and weave it into a high-scale, production-ready ecosystem. This role is for a technical visionary who will partner with our product team and our Principal Product Engineer to design the data systems that power our next generation of features. You will be the data architect on steroids, ensuring that our automated pipelines are robust, our AI outputs are reliable, and our data infrastructure scales ahead of our massive growth.

Requirements

Bachelor’s in Computer Science, Engineering, Machine Learning, or a related field; Master’s preferred.
5-10+ yrs relevant Machine Learning experience.
Demonstrated success operationalizing ML models within early-stage product ecosystems/start-up SaaS orgs.
Mathematical Depth: Strong foundation in statistics, calculus, and linear algebra. You understand the why behind the algorithms you use.
ML Development: Expert-level Python. Deep experience with libraries such as Scikit-learn, PyTorch, and Pandas. You have designed deep learning models from scratch (Vision / NLP), designed custom objective functions and fine-tuned existing models (BERT, ViT, Swin).
Data Engineering: Expert-level PostgreSQL and BigQuery. You understand how to design schemas that support both transactional integrity and analytical performance.
Production MLOps: Experience building and managing ML pipelines on GCP (Vertex AI, Cloud Run, BigQuery). You know how to monitor models for drift and performance in the wild.
TypeScript/Node.js: While you are a data specialist, you are proficient enough in our backend stack (Node/TS) to ensure your data services integrate seamlessly with the platform.
The 0-to-1 Mindset: You enjoy the ambiguity of building new things from scratch and have a track record of shipping data-centric products.
Pragmatic Architect: You know when to use a simple heuristic and when a deep learning model is required. You build for reliability and maintainability.

Nice To Haves

Experience in PropTech or Real Estate data.
Experience building internal tools or Revenue Ops automation that support sales/CS teams.

Responsibilities

Model Development & Math: Design and implement the statistical models and ML algorithms that drive our market analysis. You are comfortable with the heavy lifting of data science: linear algebra, probabilistic modeling, and predictive analytics.
Production-Grade AI/ML: Move beyond notebooks. You will architect how models are trained, versioned, and served in a production environment, ensuring high availability and low latency.
Data System Architecture: Partner with the product team and the Principal Software Engineer (Product) to design the data foundations for every new feature. You ensure that our data structures are built for scale before a single UI component is written.
Hybrid Leadership: Act as a force multiplier for our full-stack engineers. You will handle the DS-specific complexities of a feature, allowing the rest of the team to build around a robust, well-architected data engine.
Enforce Excellence: As we grow, you will define the HelloData standard for data integrity, pipeline observability, and algorithmic transparency.