Principal Software Engineer – Data Analytics

OneTrust•Atlanta, GA

18h•$139,725 - $209,588•Hybrid

About The Position

OneTrust is hiring for a role described in the posting as a Machine Learning Engineer on its AI Core team. The team builds reusable, scalable AI features and infrastructure to help business units adopt AI. The role researches, develops, and implements AI models and workflows in Python, with a focus on production-ready components and robust pipelines that can be reused across business domains. The posting title is “Principal Software Engineer – Data Analytics,” while the role description repeatedly identifies the position as a Machine Learning Engineer. The role collaborates with engineering, data science, and product teams to develop, deploy, and operationalize AI solutions. OneTrust describes its culture as office-first, encouraging three days a week in the office for most roles; specific requirements or flexibility may vary by role. Compensation for candidates based in listed jurisdictions is $139,725–$209,588 in annual base pay, with possible discretionary bonuses, equity, and/or commissions. The position may also offer comprehensive healthcare coverage, flexible PTO, equity RSUs, annual performance bonus opportunities, retirement account support, 14+ weeks of paid parental leave, career development opportunities, and company-paid privacy certification exam fees. Benefits vary by country.

Requirements

Bachelor’s or master’s degree in computer science, engineering, or a related field.
10+ years of professional software engineering experience, with a strong focus on frontend development.
Hands-on experience with one or more modern AI/ML frameworks, such as TensorFlow, PyTorch, Hugging Face, or scikit-learn.
Experience designing and deploying production-grade machine learning models and pipelines.
Familiarity with configuration-driven workflows and orchestration using tools such as YAML.
Strong testing, debugging, and documentation skills.
Excellent collaboration and communication abilities.
Curiosity and passion for AI, with a willingness to continuously learn and grow.

Nice To Haves

Experience with cloud-based ML platforms such as Azure, GCP, or AWS.
Experience with containerization using Docker.

Responsibilities

Design, build, and optimize machine learning models, pipelines, and APIs using Python and AI frameworks.
Develop reusable, production-grade components to support rapid AI solution deployment across business units.
Implement configuration-driven workflows, such as YAML, for scalable and flexible AI system orchestration.
Conduct unit, integration, and exploratory testing to maintain code quality, with a stated target of at least 80% coverage or team standards.
Partner with backend, frontend, and product teams to integrate AI models into scalable, user-facing features.
Participate in technical planning, architecture reviews, and sprint ceremonies.
Share knowledge and collaborate with team members to support continuous learning and innovation.
Monitor, evaluate, and improve AI model performance in production.
Troubleshoot and resolve issues involving data pipelines, model inference, and deployment.
Document workflows, best practices, and model configurations for reproducibility and transparency.
Conduct peer reviews and provide constructive feedback on machine learning code and processes.
Promote best practices in ML engineering, testing, and automation.
Stay current with AI/ML developments and explore new techniques, architectures, and frameworks.