ABOUT POPULOUS

For more than 40 years Populous has had a singular focus – to draw people together around the things that they love, to be the leading design firm that specialises in places, spaces and events where people gather. Our success is defined by designing projects that are seen as the benchmark. We are committed to future innovation, anticipating and shaping the future of our industries.
Our team is made up of highly talented people with a range of skills, all integral to successfully bringing to life the places and experiences we design. As our company has grown, our expertise has expanded to encompass a wide variety of disciplines – including architecture, audiovisual consultancy, brand activation, design & build, events, food & beverage strategy, interior design, landscape architecture, urban design and wayfinding.
This varied expertise enables us to transform neighbourhoods, revitalise cities, build relationships and create buildings, places and spaces that draw people together, from the Tottenham Hotspur Stadium to The Co-op Live Arena.
As we continue to bring to life award-winning venues and experiences our EMEA Practice is looking for a Machine Learning Engineer – AI Team (Global Digital) based in our Putney studio.

KEY REQUIREMENTS INCLUDE:

We’re seeking an applied machine learning engineer who loves solving real-world problems with data and AI. You’ll thrive here if you’re hands-on, curious, and excited to bring new capabilities into tools that shape spaces and human experience.

• Core Technical Skills
• Several 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)
• Experience integrating machine learning models into workflows and applications
• 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 working in cloud-based environments (AWS, Azure, or GCP)
• Development & Collaboration
• Comfortable building and maintaining ML pipelines from prototype to production
• Familiarity with tools for experiment tracking and version control (e.g., MLflow, Git, W&B)
• Strong communication skills—able to explain technical decisions to non-technical collaborators
• Effective working independently or as part of an interdisciplinary team
• Mindset & Domain Interest
• 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
• Background in architecture, engineering, construction, or location-aware applications is a bonus, not a requirement
• Preferred (but Not Required)
• Experience with LLM orchestration frameworks (e.g. LangChain, Haystack)
• Familiarity with retrieval-augmented generation (RAG), prompt tuning, or hybrid search architectures
• Exposure to MLOps workflows or orchestration tools (e.g., Airflow, Argo)
• Understanding of AI governance topics such as data privacy, fairness, and explainability
• Experience building internal tooling, design assistants, or custom AI interfaces for non-technical users

ABOUT THE ROLE

This role sits within our AI Technology Team. As a Machine Learning Engineer, you’ll help bring powerful AI capabilities to life—shaping tools that support staff across Populous. You’ll work across the full ML lifecycle, from data prep and model experimentation to deployment and ongoing optimization.
You’ll collaborate closely with full stack developers and our AI Lead to prototype, fine-tune, and integrate machine learning models—particularly in natural language processing (NLP), generative AI, and semantic search—into production systems that drive better outcomes in the built environment.

KEY RESPONSIBILITIES INCLUDE:

• Model Development & Integration
• Develop and fine-tune machine learning models, particularly in NLP, computer vision, and generative AI in collaboration with developers and data analysts as well as with design team members.
• Adapt and integrate foundation 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
• Data Pipelines & Experimentation
• 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
• Manage model evaluations, experiment tracking, and dataset versioning with reproducibility in mind
• Monitor model performance and detect drift or degradation over time
• Tooling & Infrastructure
• Use ML frameworks such as PyTorch, TensorFlow, and Hugging Face Transformers
• Operate within cloud platforms (AWS, Azure, or GCP) for model training and deployment
• Leverage tools like MLflow, Weights & Biases, or LangChain for model tracking and orchestration
• Contribute to the design and iteration of internal AI/ML powered design and productivity tools
• Collaboration & Innovation
• Work in agile, cross-functional teams alongside designers and domain experts
• Stay current on research, tooling, and trends in AI/ML - bringing new ideas into practice
• Help shape our AI architecture and share your perspective in technical planning and team discussions