Machine Learning Engineer

Triangle Analytics; New York (NY); On-site; USD 200,000 - 265,000; Full time

Job summary

Triangle Analytics seeks an ML systems engineer to build and scale machine learning components powering our core platform. You will turn signals from individuals, communities, and stakeholders into reliable predictions at scale, with ownership over modeling infrastructure.

You will work across applied ML research and production engineering, partnering with the founders and engineering team to deploy and monitor models, while balancing speed, quality, and reliability.

Qualifications

• Strong proficiency in Python and modern machine learning libraries and tooling.
• Deep understanding of ML fundamentals: feature engineering, algorithm selection, evaluation methodology.

Responsibilities

• Own the reliability, scalability, and performance of ML systems powering the product.
• Lead the full lifecycle from model experimentation to deployment in production.
• Set standards for development, deployment, monitoring, evaluation, and improvement of models.
• Collaborate with founders and engineering to own core ML architecture.

Skills

Python

ML libraries

Machine learning

Feature engineering

Production ML

Training pipelines

Evaluation pipelines

3+ years experience

Tools

AWS

GCP

SQL

Job description

Triangle is solving market failures in politics

We 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 Description

You 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 impact

You 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 haves

• Attention to detail and the instinct to produce high quality, concise, consistent, and organized code
• Strong proficiency in Python and modern machine learning libraries and tooling
• Strong machine learning and statistical modeling background
• Deep understanding of machine learning fundamentals: feature engineering, algorithm selection, evaluation methodology, etc.
• Experience building and productionizing ML models
• Experience with training, evaluation, inference, and model monitoring pipelines
• At least 3 years of industry experience; exceptional early-career candidates will also be considered

Nice to haves

Familiarity with:

• AWS, GCP, or similar cloud services
• SQL
• Databases and large-scale data processing
• MLOps and model serving infrastructure
• Experience with recommender systems, ranking, personalization, user modeling, transformers, or behavioral prediction
• Experience working in early-stage environments with broad ownership and evolving requirements

What we offer

Base salary of $200,000-$265,000 with material equity compensation.