AI/ML Engineer
Cinder · New York
About this role
## AI/ML Engineer ### About Cinder Cinder is mission-critical infrastructure that helps the world’s most important digital platforms stay true to what they stand for. The internet is increasingly abused by bad actors—and AI is making fraud, abuse, and manipulation faster and harder to fight. Cinder gives platforms a command center to **write and enforce policy**, **deploy AI agents against abuse in real time**, **investigate threats**, **file NCMEC reports**, and **prove safety programs are working**. Our customers are some of the largest internet platforms in the world, and the decisions made in our software directly determine what stays up, what comes down, and how users are treated. We’re a small, fast-moving team backed by **Accel** and **Y Combinator**. We value being intentional, direct, and deeply focused on solving real customer problems. --- ### Why this role Cinder is expanding quickly and looking for an additional **AI Engineer** to partner with our current AI Engineers, Data Scientist, and Data Engineer. You’ll build **ML systems** that make Cinder faster, more efficient, and more accurate—turning the large volume of customer decision data we process into **production models** that shape customer outcomes. We’re looking for a builder who can take models from messy data to production at scale, and who makes smart tradeoffs—reaching for **gradient-boosted trees** before transformers when appropriate, and standing up **ML infrastructure from scratch** rather than inheriting it fully built. What matters most is **judgment**: choosing the smallest, most efficient, most reliable model for the job, and understanding both ML methods and LLMs deeply enough to make the call yourself. --- ### What you’ll do - **Turn real-world customer data into learnable signals**, then choose the right approach: classical classifiers when they win on cost/latency, fine-tuned LLMs when the tradeoff is worth it, or a third-party API as a bootstrap. - **Own the full path from data to decision** (not just the model). - Improve our **classification pipeline**, **confidence cascading**, and **detection strategies** to catch harmful content efficiently—balancing **cost, latency, and accuracy**. - Develop **intelligent features** that help moderators make decisions, organize platform content, and reveal patterns across our data. - Partner with Engineering to build Cinder’s **in-house model training, hosting, and inference platform**. - Design and build **evaluation and metrics infrastructure** customers rely on, including how classifier scores and model outputs are calculated, stored, surfaced, and iterated on. - Partner with our Founding Data Scientist and AI Engineers to shape **agent evaluation architecture**—measuring whether our agent fleet makes the right decisions with the right tools at the right cost. - Partner with our Data Engineer to shape the **data infrastructure** powering ML systems, ensuring training, feature pipelines, and production inference have the right data at the right latency and scale. - **Mentor teammates** and raise the ML bar across the company as Cinder’s ML capability matures. --- ### What we’re looking for - **5–8+ years** of machine learning engineering experience on a small team, with a strong track record of shipping ML systems (e.g., **gradient boosting**, tree-based models, classifiers, embedding-based methods) to production. - You’ve taken a classification problem from **messy, unlabeled, real-world data**
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