# AI/ML Engineer at Cinder

- Company: Cinder
- What the company does: Cinder helps high-growth companies design, automate, and scale modern trust and safety operations. Backed by Accel.
- Company website: https://cinder.co/
- Type: Startups (AI role)
- Level: Mid level
- Location: New York
- Work setup: Remote
- Pay: $220K to $260K base salary per year (USD)
- Posted: 2026-08-27
- Apply by: 2026-10-11
- Apply: https://jobs.ashbyhq.com/cinder/683bb5d5-c354-4fa6-ba1f-1d54c59a470b
- Page: https://www.1752.vc/careers/jobs/cinder-ai-ml-engineer/

## About the role

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.

## What they're looking for

- 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

Tags: Product & Engineering