Principal Machine Learning Engineer- HelloData

Remote

Full Time

Experienced

Principal Machine Learning Engineer- HelloData

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.

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.

Qualifications:
Core 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.

The Data Science & Math Core

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).

The Engineering & Infrastructure Core

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.

Leadership & Philosophy

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.

Bonus Points

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

Location: Remote (US based)

Salary range: $175,000- $250,000 + Bonus
Grace Hill offers a robust suite of benefits, including health, dental and vision insurance, 401K, PTO, life insurance, disability insurance, and more.

About Us
Grace Hill provides industry-leading SaaS technology solutions designed to make a positive impact in real estate and improve the lives of people where they work and live. Harnessing years of real estate experience and the understanding that people are better together, Grace Hill helps owners and operators increase property performance, reduce operating risk and grow top talent. More than 500,000 professionals from over 1,700 companies rely on Grace Hill’s talent performance solutions covering policy, training, assessment, survey, and data-driven insights. Visit us at gracehill.com or on LinkedIn
Our HelloData product solves complex data problems for the multifamily industry, utilizing automated pipelines and AI to provide real-time market insights for the nation's top managers, developers, and investors. Our platform is trusted by the industry’s largest operators to help optimize rents, underwrite operating expenses, and grow NOI with its highly accurate data and user-friendly interface. Since being acquired by Grace Hill in April 2025, HelloData has continued to accelerate at an unbelievable rate, growing ARR by over 300% in 2025 alone and on track for a record-breaking 2026. We combine the agility and innovation of a high-growth startup with the stability and resources of an established enterprise, making us the gold standard in multifamily data analytics.

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Describe a time you owned the end-to-end lifecycle of an ML project—from raw data design to deploying a production API. What was the most difficult trade-off you had to make regarding model size vs. performance?*

Which of the following have you personally implemented and moved into a production environment? (Check all that apply)* Fine-tuning Transformer models (e.g., BERT, ViT, Swin)Containerizing ML models for Serverless (e.g., Cloud Run, Lambda)Building custom PyTorch training loops and objective functions (not just using high-level wrappers)Designing batch processing jobs for large datasetsArchitecting APIs to serve models with <200ms latencyNone of the above

In your opinion, when is a "small" model (like a distilled BERT or a simple Sklearn model) better than a SOTA Transformer in production?*

Which best describes your Python style? (Check all that apply)* Library-Heavy: I rely on Pandas, DBT, etc for almost every task.Lean: I prefer standard library Python and clean code; I only pull in heavy libraries when strictly necessary.Researcher: I primarily use Jupyter Notebooks and focus on model architecture over deployment

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Principal Machine Learning Engineer- HelloData

Principal Machine Learning Engineer- HelloData

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