Populous is looking to enhance the design and functionality of spaces by developing intelligent tools that leverage data and AI, particularly in natural language processing, generative AI, and semantic search.
Requirements
- Proficiency in Python programming and familiarity with ML libraries such as scikit-learn, PyTorch, and TensorFlow
- Experience integrating ML models into workflows and applications
- Hands-on experience with cloud platforms like AWS, Azure, or GCP
- Solid understanding of vector search, embedding systems (FAISS, Pinecone, Weaviate)
- Operationalizing models via REST APIs using frameworks like FastAPI or Flask
- Experience working with structured, unstructured, and spatial data
- Familiarity with ML frameworks such as Hugging Face Transformers
Responsibilities
- Manage the full machine learning lifecycle, from data preparation and model experimentation to deployment and optimization
- Adapt and integrate foundational models (e.g., Anthropic, OpenAI, Cohere) for specific use cases
- Develop and maintain APIs for inference, batch processing, and model access within production systems
- Collaborate with full-stack developers to embed ML capabilities into user-facing applications
- Build end-to-end data pipelines for collection, preprocessing, feature engineering, and training
- Work with diverse data types including text, images, and spatial data across various formats
- Leverage ML frameworks such as PyTorch, TensorFlow, and Hugging Face Transformers for model development
Other
- 3+ years of experience in machine learning engineering or applied ML roles
- Excellent communication skills and ability to collaborate across disciplines and cultures
- Research-oriented mindset with a desire to apply AI in impactful ways
- Interest in the built environment, urban design, and spatial data (preferred)
- Understanding of AI governance topics such as data privacy, fairness, and explainability (preferred)
