Join to apply for the Machine Learning Engineer - GenAI - Hybrid role at SitusAMC

Join to apply for the Machine Learning Engineer - GenAI - Hybrid role at SitusAMC

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Talent Acquisition Partner - Technology at SitusAMC

SitusAMC is where the best and most passionate people come to transform our client’s businesses and their own careers. Whether you’re a real estate veteran, a passionate technologist, or looking to get your start, join us as we work together to realize opportunities for everyone, we proudly serve.

At SitusAMC, we are looking to match your unique experience with one of our amazing careers, so that we can help you realize your potential and career growth within the Real Estate Industry. If you are someone who can be yourself, advocate for others, stay nimble, dream big, own every outcome, and think global but act local – come join our team!

Job Summary

The Machine Learning Engineer will be involved in the design, development, implementation, and ongoing delivery of generative AI and other machine learning solutions to enhance decision-making in commercial real estate investments. These solutions will be deployed within applications used directly by CRE professionals, making this role critical to delivering real-world, user-facing impact. The role includes formulating technical requirements based on business needs, experimenting with models, and implementing robust, production-ready solutions that transform data into actionable insights. ***This is Hybrid opening that only requires employees to be in office 1-2 times per month.

Essential Job Functions

• Provide innovative solutions for problems in commercial real estate using generative AI and other machine learning algorithms
• Use large language models (LLMs) to analyze various commercial real estate data sets, including large sets of financial documents and commercial real estate market data
• Engineer and tune LLM prompts, and perform automatic and manual evaluations of LLM outputs
• Apply machine learning algorithms to find patterns in financial data sets, especially time series, related to commercial real estate
• Translate business problems and requirements into tasks that can be solved with generative AI, machine learning, or other algorithmic solutions
• Process structured and unstructured data for generative AI use cases
• Collaborate with engineering and product development teams
• Following experimentation and model selection and validation, implement the solution using production-ready Python code
• Contribute to the full software development lifecycle, including requirements gathering, design, development, testing, deployment, and maintenance of machine learning solutions
• Monitor model performance post-deployment and iterate based on feedback
• Communicate with, and present results to, colleagues and stakeholders
• Present information using data visualization techniques
  
  

• A master’s degree (or equivalent) in a numerate discipline such as Statistics, Machine Learning, Computer Science, Engineering, or Physics. A doctorate is a plus but not required
• Mid-level professional with 2-4 years of ML/AI experience, typically at an Analyst level role or external equivalent
• A scientific approach to solving problems, an analytical mind, and experience in applied machine learning and generative AI
• Experience in applying LLMs to analyze text documents, prompt engineering, and manual and automatic evaluation of LLM outputs
• Good foundational knowledge of standard machine learning algorithms and statistical methods
• Proficient in python and its standard data science and ML libraries (e.g., numpy, pandas, scikit-learn, boosting and data visualization libraries)
• Practical experience in data processing and feature engineering for machine learning applications
• Ability to write robust production-ready Python code
• Good communication and presentation skills
• Experience in commercial real estate is a plus
  
  

Seniority level

• Seniority level

  Mid-Senior level

Employment type

• Employment type

  Full-time

Job function

• Job function

  Engineering and Information Technology

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