Formation Machine Learning Engineer
5 days ago
Pay Grade Range: $66,605.00
Disclaimer: The base salary range represents the low and high end of Altus Group’s “Pay Grade Range” for this position in the primary work location. Actual hiring salaries will vary depending on factors including but not limited to work experience, and geographic market data for the role.
If you’re looking to advance your career in data analytics, expertise, and technology for the rapidly growing global CRE market, there’s no better place than Altus Group. Every day, our employees drive impact, innovate, and shape the global commercial real estate (CRE) and PropTech industry.
Our people-centric culture empowers you to deliver in a high trust, high performance culture, surrounded by an inclusive team that’s collaborating to modernize our industry. We invest in our people with training and growth opportunities designed to propel you further in your career while providing a flexible and progressive workplace that reflects our values and teams.
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.
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.Model Development & Management: Develop and implement AI/ML models in collaboration with Data Scientists for better experiment tracking, and 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.Technical Leadership: Lead ML engineering and data pipeline projects through planning, execution, and delivery phases in partnership with stakeholders and team members.Stakeholder Communication: Communicate project status, risks, and outcomes with technical and business stakeholders, ensuring alignment on project goals and timelines.Collaboration: Work closely with Data Scientists to understand their modeling and data requirements and provide the necessary infrastructure and support.Documentation: Document ML models, data pipelines, processes, and best practices to ensure knowledge sharing and reproducibility across the team.Key Qualifications:
- Collaborative Communicator: 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: Build data pipelines and ML systems/infrastructures that are efficient, reliable, and scalable.
- Detail-Oriented Engineer: Meticulous about model performance optimization, data quality, and setting standards for deployment, monitoring and governance.
- Skilled in Writing Production Ready Code: Strong programming skills in Python, SQL, and Spark, with experience refactoring code for production readiness.
- Experienced in AI/ML Model Deployment and Platform Engineering: Comfortable working with DevOps and Data Scientists for seamless integration of AI/ML models in production.
- Experienced in Statistics Analysis/AI/Machine Learning: Familiar with a range of statistical analysis and machine learning techniques.
- 4+ years of experience 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 the financial or commercial real estate industry is a plus.
- Rewarding performance:
- Flexible work model:
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