ML Ops & Data Engineer
Build and operate the data and machine learning infrastructure behind Cherith's research in computational linguistics, Bible translation, and AI-assisted discipleship. You'll design pipelines, manage model lifecycles, and ensure the data systems that power our work are reliable, reproducible, and ready for scale.
About the Role
Cherith Labs is applying NLP and machine learning to some of the hardest problems in Bible translation and scripture engagement — automated alignment of biblical texts, large language model fine-tuning for theological precision, and AI tutoring systems that track spiritual formation. As an ML Ops & Data Engineer, you'll build the infrastructure that makes this research possible: training pipelines, data processing workflows, model deployment, and the monitoring systems that keep it all running. You'll work alongside researchers and engineers to move experiments from notebooks to production.
Responsibilities
- Design and maintain data pipelines for processing multilingual scripture texts, linguistic datasets, and training corpora
- Build and manage ML training and inference infrastructure — experiment tracking, model versioning, and deployment
- Develop ETL workflows for structured and unstructured text data across Bible translation projects
- Implement monitoring, logging, and alerting for deployed models and data systems
- Collaborate with researchers to operationalize NLP experiments and LLM fine-tuning workflows
- Manage cloud infrastructure and optimize for cost, reliability, and reproducibility
Requirements
- 3+ years of experience in data engineering, ML ops, or infrastructure engineering
- Proficiency in Python and familiarity with ML frameworks (PyTorch, HuggingFace, or similar)
- Experience with data pipeline tools (Airflow, Dagster, dbt, or similar)
- Comfort with cloud platforms (AWS or GCP) — compute, storage, and orchestration services
- Understanding of ML model lifecycle: training, evaluation, deployment, and monitoring
- Alignment with Cherith's mission and doctrinal foundation
Nice to Have
- Experience with NLP pipelines or text processing at scale
- Familiarity with LLM fine-tuning, RLHF, or retrieval-augmented generation (RAG)
- Background in computational linguistics or working with multilingual datasets
- Experience with containerization (Docker, Kubernetes) and CI/CD for ML systems
Interested in this role?
Send us a note with a bit about yourself and why this role caught your eye. No formal cover letter required.
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