Research Engineer III

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. 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 communications platforms and the world’s leading cloud infrastructure platforms. We use the latest in AI/ML technology to help our customers break new ground at scale. We are a global organization that values diversity, and we believe that providing opportunities for everyone to be their authentic self is key to our success. Smarsh leadership, culture, and commitment to developing our people have all garnered Comparably.com Best Places to Work Awards. Come join us and find out what the best work of your career looks like. country: US all locations: [US - Remote] commitment: Intern - Paid department: Divisions location: US - Remote team: Applied Machine Learning 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