Key Facts

Remote From:

United States

Category: Research Assistant

Internships

Entry-level / graduate

English

Hard Skills

Apache Airflow AWS SageMaker Terraform MLflow Natural Language Processing (NLP) Observability PyTorch (Machine Learning Library) Model Validation Amazon Virtual Private Cloud (VPC) CI/CD +9 more

Other Skills

•
Collaboration
•
Problem Solving

Roles & Responsibilities

Experience with PyTorch, transformers, or other ML libraries
Familiarity with ML model evaluation and experimentation
Knowledge of AWS services (S3, SageMaker, IAM)
Exposure to Apache Airflow or workflow orchestration

Requirements:

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)

Cognia

About Cognia

Smarsh enables companies to transform oversight into foresight by surfacing business-critical signals in more than 100 digital communications channels. Regulated organizations of all sizes rely upon the Smarsh portfolio of cloud-native digital communications capture, retention, and oversight solutions to help them identify regulatory and reputational risks within their communications data before those risks become fines or headlines. Smarsh serves a global client base spanning the top banks in North America, Europe, and Asia, along with leading brokerage firms, insurers, and registered investment advisors and U.S. state and local government agencies.

Company type: XLarge

Founded: 2018

Company size: 1001 - 5000

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Job 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

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.

Ready to apply?

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