Production ML Engineer — Real‑Time Safety & Scale

Cinder Technologies; New York (NY); On-site; USD 150,000 - 230,000; Full time

Benefits offered by this job

Health benefits

Vision & dental

401(k) match

Commuter benefits

Lunch and dinner onsite

Relocation assistance

Job summary

Cinder Technologies is seeking an AI Engineer to build production ML systems that scale decision-making from real-world data to deployed models. You will balance precision-recall, latency, and cost while designing end-to-end ML pipelines.

You will partner with data scientists, data engineers, and other ML engineers to deploy, evaluate, and monitor models, boosting moderation capabilities and platform performance. NYC relocation is offered with in-person collaboration.

Qualifications

• 5–8+ years of ML engineering on a small team with production ML experience.
• Took a classification problem from messy data to deployed model in production.
• Understands LLM tradeoffs versus classic models for latency and cost.
• Hands-on experience building classifiers under severe data imbalance.
• Built training pipelines, serving infra, evaluation harnesses from scratch.
• Startup or small company experience with pragmatic tradeoffs on build vs buy.
• Strong fundamentals: feature engineering, train/test splits, imbalanced data metrics.
• Strong Python and ML frameworks: PyTorch, scikit-learn, LangChain, XGBoost.
• Solid MLOps: CI/CD, model versioning, experiment tracking, drift detection, production monitoring; Databricks a plus.
• Latency-aware inference design; tradeoffs between model complexity, cost, and performance.
• AWS experience and Terraform = plus.

Responsibilities

• Turn real-world customer data into learnable features and select the model approach (classical vs fine-tuned LLM).
• Improve classification pipeline, confidence cascading, and detection strategies for efficiency.
• Develop features to aid moderators and reveal patterns across data.
• Partner with Engineering to build in-house training, hosting, and inference platform.
• Design evaluation and metrics infrastructure for classifier scores and outputs.
• Collaborate with Founding Data Scientist to shape agent evaluation architecture.
• Coordinate with Data Engineer to ensure data infrastructure supports training and inference at scale.
• Mentor teammates and raise ML standards across the company.

Skills

ML engineering

Production ML

Gradient boosting

Tree-based models

Classification

LLMs

Python

PyTorch

scikit-learn

XGBoost

LangChain

MLOps

Databricks

Terraform

Tools

Databricks

LangChain

PyTorch

scikit-learn

XGBoost

Job description

Cinder Technologies is seeking an AI Engineer to build production ML systems that scale decision-making from real-world data to deployed models. You will balance precision-recall, latency, and cost while designing end-to-end ML pipelines.

You will partner with data scientists, data engineers, and other ML engineers to deploy, evaluate, and monitor models, boosting moderation capabilities and platform performance. NYC relocation is offered with in-person collaboration.