AI/ML Engineer
Build and operate production machine-learning systems for content safety, from messy customer data through classification, evaluation, and inference. The role requires 5+ years of ML engineering experience, strong Python and MLOps skills, and sound judgment across classical models and LLMs.
About the job
Responsibilities
- Build production ML systems from messy customer data through model training, serving, and decisioning.
- Improve classification pipelines, confidence cascading, and detection strategies while balancing cost, latency, and accuracy.
- Develop intelligent features to support moderation decisions, organize platform content, and identify patterns in data.
- Partner with Engineering on in-house model training, hosting, and inference infrastructure.
- Design evaluation and metrics infrastructure for classifier scores and model outputs.
- Help shape agent evaluation architecture, including decision quality, tool usage, and cost measurement.
- Collaborate on data infrastructure for training, feature pipelines, and production inference.
- Mentor teammates and raise the company's ML engineering standards.
Requirements
- 5–8+ years of machine learning engineering experience on a small team, with a track record of shipping ML systems to production.
- Experience taking classification problems from messy, unlabeled, real-world data to production-served models.
- Strong understanding of when to use LLMs versus classical models based on cost, latency, and performance.
- Hands-on experience building classifiers with severe class imbalance.
- Experience building ML infrastructure from scratch, including training pipelines, serving infrastructure, evaluation harnesses, and monitoring.
- Startup or small/mid-size company experience with meaningful ownership and pragmatic build-versus-buy decisions.
- Strong knowledge of feature engineering, leak-aware train/test splits, metric selection for imbalanced data, cross-validation, and hyperparameter tuning.
- Strong Python skills and experience with AI/ML frameworks.
- MLOps experience including CI/CD for ML, model versioning, experiment tracking, drift detection, and production monitoring.
- Experience designing inference systems with latency and throughput targets.
Nice-to-haves
- Experience training, evaluating, and serving models with Databricks.
- Experience with AWS and Terraform.
Compensation and Benefits
- Annual salary range of $220,000–$260,000.
- Health, vision, and dental benefits.
- 401(k) plan with employer matching.
- Fully paid commuter benefits.
- Fully stocked office with paid lunch and dinner.
- Relocation support for the New York City area.
Skills
Python, PyTorch, scikit-learn, LangChain, Xgboost, MLOps, CI/CD, Terraform, AWS, Databricks, Model Monitoring, Experiment Tracking
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