AI/ML Engineer

Cinder•New York, NY

1d•Onsite

About The Position

Cinder is the mission-critical infrastructure that keeps the world's most important digital platforms true to what they stand for. The internet has always been abused by bad actors, and AI is making it exponentially worse, driving fraud, abuse, and manipulation at a scale and speed no human team can fight alone. Cinder gives platforms one command center to fight back: to write and enforce policy, deploy AI agents against abuse in real time, investigate threats, file NCMEC reports, and prove their safety programs are working. Our customers are some of the largest internet platforms in the world. 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 care about being intentional, direct, and deeply focused on solving real customer problems. Cinder is expanding quickly, and we want to bring on an additional AI Engineer to partner with our current AI Engineers, Data Scientist, and Data Engineer. You'll build the ML systems that make Cinder faster, more efficient, and more accurate, turning the enormous volume of customer decision data we process into production models that directly shape customer outcomes. We're looking for a builder. Someone who has taken models from messy data to production at scale, who reaches for a gradient-boosted tree before a transformer when that's the right call, and who has stood up ML infrastructure from scratch rather than inheriting it fully-built. What matters most is judgment: knowing the smallest, most efficient, most reliable model for the job, and understanding both ML methods and LLMs deeply enough to make that call yourself rather than defaulting to whichever one you know best. This role needs someone who cares as much about precision-recall tradeoffs, class imbalance, and serving latency as they do about model architecture.

Requirements

5–8+ years of machine learning engineering experience on a small team, with a strong track record of shipping ML systems (gradient boosting, tree-based models, classifiers, embedding-based methods) to production.
You've taken a classification problem from messy, unlabeled, real-world data all the way to a model that shipped and served production traffic.
You understand LLMs well enough to make an informed, defensible call about when an LLM is worth its cost and latency versus a classic model.
Real, hands-on experience building classifiers under severe class imbalance, where the signal you care about is a small minority of the data
Thrived in environments where the ML infrastructure wasn't already built for you: you've stood up training pipelines, serving infrastructure, evaluation harnesses, and monitoring from scratch rather than inheriting a mature platform.
Startup or small/mid-size company experience where you owned meaningful scope and had to make pragmatic tradeoffs about what to build, what to buy, and what to defer.
Deep fluency with the fundamentals: thoughtful feature engineering, leak-aware train/test splits, metric selection on imbalanced data (precision/recall/F1/AUC over accuracy), cross-validation, and principled hyperparameter tuning.
Strong Python skills and hands-on experience with AI & ML frameworks: (PyTorch, scikit-learn, langchain, XGBoost etc)
Solid MLOps foundation: CI/CD for ML, model versioning, experiment tracking, drift detection, and production monitoring.
Experience designing inference systems with explicit latency and throughput targets, and independently making informed tradeoffs between model complexity, cost, and performance.

Nice To Haves

Bonus if you have experience with training, evaluating and serving models via Databricks
Experience with AWS and infrastructure-as-code (Terraform) is a plus.

Responsibilities

Turn real-world customer data into something a model can actually learn from, then decide what model approach fits: a classical classifier when it wins on cost and latency, a fine-tuned LLM when the tradeoff is worth it, a third-party API as a bootstrap. You own the full path from data to decision, not just the model.
Improve our classification pipeline, confidence cascading, and detection strategies so we catch harmful content efficiently — balancing cost, latency, and accuracy deliberately.
Develop intelligent features that help moderators make decisions, organize platform content, and reveal patterns across our data.
Partner with Engineering to build out Cinder's in-house model training, hosting, and inference platform.
Design and build the 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 the agent evaluation architecture — measuring whether our agent fleet is making the right decisions with the right tools at the right cost.
Partner with our Data Engineer to shape the data infrastructure powering our ML systems, ensuring model training, feature pipelines, and production inference have the right data flowing at the right latency and scale.
Mentor teammates and raise the ML bar across the company as Cinder's ML capability matures.