AI/ML Engineer
$220k - $260k • New York • FullTime
Posted 1mo ago
Job Location
New York
Tech Stack
Remote Work Policy
On-site
Employment Type
FullTime
Categories
Machine Learning Engineer
About the job
Cinder is seeking an AI Engineer to join their fast-moving team, backed by Accel and Y Combinator. This role is crucial for building the ML systems that enhance Cinder's speed, efficiency, and accuracy. You will transform vast amounts of customer decision data into production models that directly impact customer outcomes. The ideal candidate is a builder who has successfully taken models from raw data to production at scale, possesses strong judgment in selecting the most efficient and reliable model for a given task, and has a deep understanding of both ML methods and LLMs. This position requires a keen focus on precision-recall tradeoffs, class imbalance, and serving latency, alongside model architecture.
Responsibilities
- Transform real-world customer data into model-learnable formats and select appropriate model approaches (e.g., classical classifiers, fine-tuned LLMs, third-party APIs).
- Own the end-to-end process from data preparation to model deployment and decision-making.
- Enhance classification pipelines, confidence cascading, and detection strategies to efficiently identify harmful content while balancing cost, latency, and accuracy.
- Develop intelligent features to assist moderators, organize content, and uncover data patterns.
- Collaborate with Engineering to build and expand Cinder's in-house model training, hosting, and inference platform.
- Design and construct evaluation and metrics infrastructure for customer reliance, including score calculation, storage, surfacing, and iteration.
- Partner with Data Scientists and AI Engineers to define agent evaluation architecture, measuring agent performance, tool usage, and cost-effectiveness.
- Collaborate with Data Engineers to shape the data infrastructure for ML systems, ensuring efficient data flow for training, feature pipelines, and inference.
- Mentor team members and elevate the company's ML capabilities.
Requirements
- 5-8+ years of machine learning engineering experience, particularly on small teams with a proven track record of shipping ML systems (gradient boosting, tree-based models, classifiers, embedding-based methods) to production.
- Experience taking classification problems from messy, unlabeled, real-world data to production-served models.
- Sufficient understanding of LLMs to make informed decisions about their cost and latency tradeoffs compared to classic models.
- Hands-on experience building classifiers for severely imbalanced datasets where the target signal is a small minority.
- Experience building ML infrastructure (training pipelines, serving infrastructure, evaluation harnesses, monitoring) from scratch in startup or small/mid-size company environments.
- Demonstrated ability to make pragmatic tradeoffs regarding scope and build vs. buy decisions.
- Deep fluency in ML fundamentals, including feature engineering, leak-aware train/test splits, metric selection for imbalanced data (precision/recall/F1/AUC), cross-validation, and principled hyperparameter tuning.
- Strong Python skills and hands-on experience with AI/ML frameworks like PyTorch, scikit-learn, langchain, and XGBoost.
- Solid MLOps foundation including CI/CD for ML, model versioning, experiment tracking, drift detection, and production monitoring.
- Experience designing inference systems with specific latency and throughput targets, balancing model complexity, cost, and performance.
- Experience with AWS and infrastructure-as-code (Terraform) is a plus.
- Experience with training, evaluating, and serving models via Databricks is a bonus.
Benefits
- Health, vision, and dental benefits
- 401(k) plan with employer matching
- Fully paid commuter benefits
- Fully stocked office with paid lunch and dinner