AI/ML Engineer

About the role

Cinder is expanding quickly and is seeking an AI Engineer to join our team. This role will work closely with our current AI Engineers, Data Scientist, and Data Engineer to build and improve the ML systems that power our platform.

Responsibilities

• Turn real-world customer data into something a model can 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.
• 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.

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.
• Bonus if you have experience with training, evaluating and serving models via Databricks
• Experience designing inference systems with explicit latency and throughput targets, and independently making informed tradeoffs between model complexity, cost, and performance.
• Experience with AWS and infrastructure-as-code (Terraform) is a plus.

Qualifications

• Location & Benefits
• We're based in NYC and will relocate for this role.
• We believe in working together in person and hold at least two all-company events per year.
• We offer health, vision & dental benefits, a 401(k) plan with employer matching, fully paid commuter benefits, and a fully stocked office with paid lunch and dinner.
• Compensation Range: $220K - $260K