Machine Learning Engineer

New Yesterday

Machine Learning Engineer
Location: New York
Who We Are:
We design places where people love to be together.
Populous is a global design firm that began with a singular focus: to draw people together around the things they love, through experiences that capture all the senses, amplifying the atmosphere of excitement and pure joy shared in human moments.
We've designed and delivered some of the world's most memorable civic, sports and entertainment buildings, from iconic stadia to ground-breaking live music venues. Populous is where architects and designers of all kinds create moments bigger than ourselves. We offer the best of both worlds: the resources and impact of the design giants and the tightly knit atmosphere and growth opportunities of smaller firms.
What We Offer:
Professional & Personal Development Programs + Summer Hours + Hybrid Schedule + On-Site Gym + Paid Architect Licensure & Certifications Exams + Wedding Pay + Charitable Match Program + Market Leading Wellness Health and Welfare Benefits.
Who We Are Looking For:
We're seeking a Machine Learning Engineer with at least 3 years of experience in applied ML to join our Global AI Technology team.
Are you an ML engineer who loves solving real-world problems with data and AI? You'll thrive at Populous if you're hands-on, curious, and excited to bring new AI capabilities into tools that shape spaces and human experience.
Collaborating across time zones with full stack developers and our AI Lead, you'll help 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.
You'll join a passionate and forward-thinking team using the latest tools and cloud technologies to work on projects at the intersection of design, data, sports and entertainment, and AI.
What Your Day Could Consist Of: 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. 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. Help shape our AI architecture, staying current on research, tooling, and trends in AI/ML - sharing your perspective in technical planning and team discussions. Requirements For Success:
Core Technical Skills 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). Familiarity with retrieval-augmented generation (RAG), prompt tuning, or hybrid search architectures is preferred. Exposure to MLOps workflows or orchestration tools (e.g. Airflow, Argo) is preferred. 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). 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. Understanding of AI governance topics such as data privacy, fairness, and explainability is preferred. Comfortable collaborating across disciplines, time zones, and cultures in a hybrid or remote setting. Essential Qualifications:
Required 3+ years of experience in machine learning engineering or applied ML roles. Experience integrating machine learning models into workflows and applications. Experience working in cloud-based environments (AWS, Azure, or GCP). Preferred Experience in the AEC (architecture, engineering, construction) industry or in location-aware applications. Experience with LLM orchestration frameworks (e.g. LongChain, Haystack). Experience building internal tooling, design assistants, or custom AI interfaces for non-technical users. Additional details: Travel may be required.
Compensation:
The projected salary range for this opportunity is $105,000 - $124,000 annually, and may be more or less depending on skills, experience and education.
Populous offers a competitive salary and bonus packages. We strive to offer our staff the best benefits package in the industry, at the lowest cost to employees, including medical, dental and vision coverage, 401k, FSA/HSA, paid time off and continuing education benefits. Populous is an equal opportunity employer. We consider all qualified applicants for employment without regard to race, religion, color, national origin, sex, age, genetic information, sexual orientation, veteran status, disability status, or any other characteristic protected under applicable federal, state, or local laws.
No. 1 Most Innovative Company in Architecture by Fast Company
No. 1 Firm in Cultural on Interior Design magazine's Top 100 Giants
No. 1 Architecture Firm in Cultural Facilities by BD+C
No. 1 Architecture Firm in Convention Centers by BD+C
No. 1 Architecture Firm in Performing Arts and Concert Venues by BD+C
Find us here - https://populous.com/careers
Instagram: @WeArePopulous
X: @Populous
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Location:
New York, NY, United States
Category:
Computer And Mathematical Occupations

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