Machine Learning Engineer

Company Description Triage Ops is building TRIAGE, an intelligent operations layer that helps teams prioritize, route, and resolve high-volume workflows using AI across tasks, calls, messages, and back-office queues. Instead of simply adding chatbots to existing systems, TRIAGE focuses on understanding incoming work, classifying urgency, assigning ownership, and recommending next actions. The platform can trigger AI voice calls to handle inbound and outbound communication, collect missing information, qualify leads, escalate urgent cases, and provide structured updates to teams. Triage Ops works with operations-heavy businesses where speed, routing, follow-up, and accountability directly impact revenue and customer experience. The team is focused on solving real operational challenges with practical, reliable AI solutions.

Role Description This is a full-time, on-site Machine Learning Engineer role based in the New York City Metropolitan Area. The Machine Learning Engineer will design, build, and deploy machine learning models that power TRIAGE’s ability to understand, classify, and route high-volume operational workflows. Day-to-day responsibilities include developing and optimizing algorithms for pattern recognition across multi-channel data (tasks, calls, messages, and tickets), training and evaluating neural network models, and integrating ML components into production systems in collaboration with software engineers. The role involves working closely with product and operations teams to translate business requirements into technical solutions, monitoring model performance, and iterating on models to improve accuracy, speed, and reliability. The engineer will contribute to data pipelines, experiment design, and documentation to ensure scalable and maintainable ML systems.

Qualifications

• Strong foundation in Computer Science concepts and Algorithms, with the ability to design efficient, production-ready solutions.
• Practical experience in Pattern Recognition and Neural Networks, including training, evaluating, and deploying models on real-world data.
• Proficiency in Statistics and applied data analysis to design experiments, interpret results, and improve model performance.
• Hands-on experience with machine learning frameworks and tools (e.g., PyTorch, TensorFlow, scikit-learn) and programming in Python or similar languages.
• Experience building end-to-end ML pipelines, including data preprocessing, feature engineering, model serving, and monitoring in a production environment.
• Familiarity with cloud platforms and distributed systems (e.g., AWS, GCP, or Azure) for scalable training and deployment.
• Ability to collaborate with cross-functional teams, communicate technical concepts clearly, and translate operational needs into ML solutions.
• Bachelor’s or advanced degree in Computer Science, Machine Learning, Statistics, or a related field, or equivalent practical experience.
• Experience with NLP, voice or call handling systems, or operations-focused products is a plus.