Populous is looking to integrate AI capabilities into tools that shape spaces and human experience, aiming to drive better outcomes in the built environment through machine learning.
Requirements
- 3+ years of experience in machine learning engineering or applied ML roles.
- Strong Python programming skills and familiarity with ML libraries (e.g. scikit-learn, PyTorch, TensorFlow).
- Solid understanding of vector search and embedding-based systems (e.g. FAISS, Pinecone, Weaviate).
- Comfortable operationalizing models via REST APIs (e.g. using FastAPI or Flask).
- Proficient in handling both structured and unstructured data (text, images, spatial data).
- Experience integrating machine learning models into workflows and applications.
- Experience working in cloud-based environments (AWS, Azure, or GCP).
Responsibilities
- Working across the full ML lifecycle, from data prep and model experimentation to deployment and ongoing optimization.
- Adapt and integrate foundational models (e.g. Anthropic, OpenAI, Cohere) for targeted use cases.
- Implement and maintain APIs for inference, batch jobs, and model access within production systems.
- Collaborate with developers to embed ML capabilities in user-facing applications.
- Build end-to-end pipelines for data collection, preprocessing, feature engineering, and training.
- Work with structured, unstructured, and spatial data across a variety of formats and sources.
- Use ML frameworks such as PyTorch, TensorFlow, and Hugging Face Transformers.
Other
- Excellent communication skills – able to explain technical decisions to non-technical collaborators.
- Research-oriented and self-motivated with a desire to apply AI in tangible, impactful ways.
- Interest in the built environment – whether through urban design, spatial data, or large-scale civic infrastructure.
- Comfortable collaborating across disciplines, time zones, and cultures in a hybrid or remote setting.
- Travel may be required.
