Senior AI/ML Engineer — Production-Scale Moderation AI

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

Benefits offered by this job

Health benefits

Vision & dental

401(k) plan

Commuter benefits

Free lunch & dinner

Job summary

CinderCinder is expanding its AI team to build production ML systems that affect real-world moderation decisions. You will transform customer data into learnable signals, select optimal models, and deploy reliable classifiers and LLMs where appropriate.

Join a small, fast-moving team in NYC to own end-to-end ML infrastructure, from data pipelines to monitoring, with relocation support for in-person collaboration and rapid iteration.

Qualifications

• 5–8+ years of ML engineering experience shipping ML systems to production.
• Proven ability to take a classification problem from messy data to a deployed model.
• Deep understanding of when to use LLMs vs classical models.
• Hands-on experience building classifiers under severe class imbalance.
• Experience standing up training pipelines, serving infra, evaluation harnesses, and monitoring.

Responsibilities

• Turn real-world customer data into learnable features and choose the right model approach.
• Improve classification pipelines, confidence cascading, and detection strategies for efficiency.
• Develop features to aid moderators and reveal patterns across data.
• Collaborate to build Cinder's in-house training, hosting, and inference platform.
• Design evaluation and metrics infrastructure for scoring model outputs.

Skills

Machine learning

ML systems

Python

PyTorch

scikit-learn

XGBoost

MLOps

Langchain

LLMs

AWS

Education

Bachelor's or Master’s degree in CS or related field

Tools

Databricks

Terraform

CI/CD for ML

Experiment tracking

Model versioning

Job description

CinderCinder is expanding its AI team to build production ML systems that affect real-world moderation decisions. You will transform customer data into learnable signals, select optimal models, and deploy reliable classifiers and LLMs where appropriate.

Join a small, fast-moving team in NYC to own end-to-end ML infrastructure, from data pipelines to monitoring, with relocation support for in-person collaboration and rapid iteration.