Machine Learning Engineer

Triangle is solving market failures in politicsWe are Triangle Analytics, a startup that delivers behavioral intelligence to the key stakeholders behind new infrastructure development, backed by General Catalyst and Pear VC. Today, communities, public officials, and developers are often making major decisions while flying blind, relying on fragmented public data, polling, and intuition. That makes it difficult to understand what communities truly care about, where interests align, and how projects can create shared value. We are building systems that model community sentiment, helping stakeholders understand the drivers of support or opposition, engage earlier, and better align incentives through smarter outreach, project design, and community benefit frameworks. Triangle is deployed in civic use cases across the country, including a $275 million bond project, where our models guide community engagement.Role DescriptionYou will be responsible for building the machine learning systems that power our core platform. Your work will focus on turning fragmented signals about individuals, communities, and stakeholders into reliable behavioral predictions at scale. This includes developing new modeling approaches, building training and inference pipelines, integrating structured and unstructured data, deploying and monitoring models, and developing the infrastructure needed to continuously evaluate and improve model performance. This is a role for someone who wants to work across applied ML research and production engineering, developing new models and building the systems required to deploy them at scale. You will work closely with the founders and engineering team and have meaningful ownership over our core machine learning architecture.Responsibilities and potential impactYou will own the reliability, scalability, and performance of the machine learning systems that power the product. This means leading the full lifecycle, from driving model experimentation to deploying and scaling models in production. You will set the standards for how models are developed, deployed, monitored, evaluated, and improved, and make tradeoffs between speed, model quality, reliability, and engineering complexity.For the organizations we serve, this work is mission critical. Stakeholder intelligence directly shapes how teams allocate resources, prioritize engagement, and make high-stakes decisions. Most organizations today experience this as friction: fragmented data, inconsistent analysis, and slow feedback loops. The systems you help build will allow teams to continuously turn behavioral signals into reliable predictions, understand what matters earlier, and act with far greater clarity and speed. Over time, this creates a more scalable and effective way to understand stakeholder behavior.Must havesAttention to detail and the instinct to produce high quality, concise, consistent, and organized codeStrong proficiency in Python and modern machine learning libraries and toolingStrong machine learning and statistical modeling backgroundDeep understanding of machine learning fundamentals: feature engineering, algorithm selection, evaluation methodology, etc. Experience building and productionizing ML modelsExperience with training, evaluation, inference, and model monitoring pipelinesAt least 3 years of industry experience; exceptional early-career candidates will also be consideredNice to havesFamiliarity with:AWS, GCP, or similar cloud servicesSQLDatabases and large-scale data processingMLOps and model serving infrastructureExperience with recommender systems, ranking, personalization, user modeling, transformers, or behavioral prediction Experience working in early-stage environments with broad ownership and evolving requirementsWhat we offerBase salary of $200,000–$265,000 with material equity compensation.