ResearchEngineerIII

## Description Who are we? Smarsh empowers its customers to manage risk and unleash intelligence in their digital communications. Our growing community of over 6500 organizations in regulated industries counts on Smarsh every day to help them spot compliance, legal or reputational risks in 80+ communication channels before those risks become regulatory fines or headlines.  Relentless innovation has fueled our journey to consistent leadership recognition from analysts like Gartner and Forrester, and our sustained, aggressive growth has landed Smarsh in the annual Inc. 5000 list of fastest-growing American companies since 2008. Join our team building production ML infrastructure for enterprise-scale machine learning pipelines.You'll work on a platform that orchestrates end-to-end ML workflows from data ingestion through model training,  evaluation, and deployment. ## How will you contribute? Build and maintain Apache Airflow DAGs for ML pipeline orchestration Develop SageMaker training jobs for NLP models (NeMo, PyTorch) Implement MLflow tracking and model registry integrations Write infrastructure-as-code using Terraform (AWS S3, IAM, VPC) Create comprehensive tests for ML pipeline components Follow spec-driven development practices with Claude Code Contribute to ML observability and evaluation frameworks ## What will you bring? Experience with PyTorch, transformers, or other ML libraries Familiarity with ML model evaluation and experimentation Interest in ML/AI infrastructure and operations Strong problem-solving and debugging skills Comfortable with Linux/command-line environments Knowledge of AWS services (S3, SageMaker, IAM) Exposure to Apache Airflow or workflow orchestration Understanding of CI/CD, testing, or infrastructure-as-code ## Additional Information About our culture Smarsh hires lifelong learners with a passion for innovating with purpose, humility and humor. Collaboration is at the heart of everything we do. We work closely with the most popular comm