Machine Learning Engineer (Remote)

Job Summary:

Altus Group is seeking a Machine Learning (ML) Engineer to support Altus Labs, the firm's commercial real estate (CRE) hub for innovation, focused on applying new data, methodologies, and tech to drive better client investment and operating outcomes. As an ML Engineer, you will develop, deploy, and manage data pipelines and machine learning models, ensuring they are production-ready and scalable. You will collaborate with Data Scientists, Analysts, DevOps Engineers and other stakeholders to drive better client investment and operating outcomes through advanced machine learning techniques.

Key Responsibilities:

• MLOps Strategy: Develop and implement MLOps strategies, best practices, and standards to enhance the efficiency of end-to-end model lifecycle activities.

• ML System Design and Development: Design and develop batch and streaming-based AI/ML pipelines including data ingestion, feature engineering, model training and scaled AI model compute.

• Data Integration: Integrate data from various sources, including structured and unstructured data, to create comprehensive datasets for analysis.

• Pipeline Development: Design, build, and maintain scalable data pipelines using Databricks, ensuring data is accessible and reliable with quality and version control for analytics and machine learning models; Create continuous integration (CI), continuous delivery (CD), and continuous training (CT) pipelines for ML systems to improve teams’ efficiency and ensure consistency and reproducibility of data and model pipelines.

• Model Development & Management: Develop and implement AI/ML models in collaboration with Data Scientists for better experiment tracking, and to set up the model registry to manage the model lifecycle including staging, serving, production and storing model artifacts.

• Model Deployment: Deploy models into production environments, ensuring they are scalable, reliable, and reusable.

• Model Monitoring: Monitor model performance, detect anomalies, and retrain models as necessary.

• Software Engineering: Write robust and efficient code to integrate ML models into applications and systems.

• Performance Optimization: Optimize machine learning models and data pipelines for compute, performance, efficiency and scalability, ensuring efficient data processing, model training and serving, with minimal downtime.

• Technical Leadership: Leading ML engineering and data pipeline projects through planning, execution, and delivery phases in partnership with stakeholders and team members.

• Stakeholder Communication: Communicating project status, risks, and outcomes with technical and business stakeholders, ensuring alignment on project goals and timelines.

• Collaboration: Working closely with Data Scientists to understand their modeling and data requirements and provide the necessary infrastructure and support.

• Documentation: Documenting ML models, data pipelines, processes, and best practices to ensure knowledge sharing and reproducibility across the team.

Key Qualifications: 

• Collaborative Communicator: You can work independently but enjoy collaborating with a broader team to share ideas, questions, and insights regarding machine learning and data engineering.

• Focused on Efficiency and Reliability: You focus on building data pipelines and ML systems/infrastructures that are efficient, reliable, and scalable, ensuring smooth data operations and robust model performance.

• Detail-Oriented Engineer: You are meticulous about model performance optimization, data quality, and setting standards for deployment, monitoring and governance.

• Skilled in writing production ready code: You have strong programming skills at least in Python, SQL, and Spark, and have prior hands-on experience to refactor the messy codes from Data Scientists to make it become production ready, which is unit tested, reusable, easily maintained and debugged. Having prior experience with Machine Learning Python libraries such as MLflow, TensorFlow, PyTorch, and Scikit-Learn is the plus

• Experienced in Versioning and Experiment Tracking: You know the best practice of using Github for branching strategy, code versioning and code review. You have hands-on experience using tools such as MLflow for experiment tracking and model registry, including tracking the model development process and saving code snapshots, model parameters, metrics, and other metadata. Having prior experience with Databricks Experiments is a plus.

• Experienced in AI/ML Model Deployment and Platform Engineering: You are comfortable to work with DevOps team and Data Scientists to understand Altus’s infrastructure and software/libraries/model inference requirements for testing and deploying AI/ML models in production so as to ensure seamless integration with existing systems/products, and to guide Data Scientists through the best practices.  Having prior experience with Databricks Unity Catalog & Catalogs, Databricks Asset Bundles (DABs), containerization (e.g., Docker) is a plus.

• Experienced in Statistics Analysis/AI/Machine Learning: To facilitate the discussions with Data Scientists, you are familiar with (or can learn quickly) a range of statistical analysis and machine learning techniques, including hypothesis testing, linear/logistic regression, time series analysis, tree-based models, KNN, neural networks, clustering, and outlier/anomaly detection. Prior experience with NLP, transfer learning, large language models (LLMs), and Generative AI is a plus.

• 4+ years of experience working in an ML engineering or data engineering position.

• BS or MS degree in Computer Science, Engineering, Applied Mathematics, or a related technical field.

• Experience with financial or commercial real estate industry is a plus.