• Contribute to the development of scalable algorithms for processing large datasets • Implement and evaluate machine learning and deep learning algorithms for Natural Language Processing/Understanding • Assist in the design, development, and testing of ML models and pipelines for real-time processing of unstructured data • Collaborate with senior team members to analyze the impact of algorithm changes • Learn and grow under mentorship, gaining exposure to industry-standard NLP tools and techniques

• Contribute to the development of scalable algorithms for processing large datasets, implement and evaluate machine learning and deep learning algorithms for Natural Language Processing/Understanding. • Assist in the design, development, and testing of ML models and pipelines for real-time processing of unstructured data. • Collaborate with senior team members to analyze the impact of algorithm changes. • Learn and grow under mentorship, gaining exposure to industry-standard NLP tools and techniques.

• Architect and build production systems across a multi language stack: C#/.NET services, Python AI and data pipelines, and occasional TypeScript • Set the example for an AI forward, high technical standard across the broader engineering organization, leading through craft rather than organization wide authority • Design and drive adoption of offline evaluation frameworks (test suites, golden datasets, regression harnesses wired into CI/CD) for LLM based and other non deterministic components • Build online evaluation and monitoring that continuously assesses live model and system behavior in production, including drift and regression detection • Partner with engineering leadership to surface systemic risks in AI features, such as reliability, correctness, and drift, and drive the fixes • Lead architecture reviews for major initiatives and mentor senior and staff engineers on system design, evaluation methodology, and AI forward practices • Prototype and ship new AI capabilities

• Alpha-to-production ownership — take newly built agents from working v1 to production-ready: write evals, harden prompts and tool definitions, and refactor prototype scaffolding into clean, production-quality code. • Observability & stability — instrument tracing, logging, and monitoring across the agent fleet so we catch regressions before our 120+ GTM users do. • Deployment & infrastructure — stand up and maintain the pipelines that get agents into users' hands without surprises, owning environment parity and release hygiene on our GCP stack. • Database administration — own how agents read from and write to our databases, safely and at scale. • Fleet maintenance — monitor agent behavior in production, triage issues, and ship the steady stream of improvements that make an agent indispensable rather than just useful. • Partner closely with the GTM AI team — free up our agent builders to stay in their zero-to-one wheelhouse while you own scale and reliability. • Bring an outside point of view — tell us what we built wrong, and back it up with a plan to fix it.