Company Description
When you join Turnitin, you’ll be welcomed into a company that is a recognized innovator in the global education space. For over 25 years, Turnitin has partnered with educational institutions to promote honesty, consistency, and fairness across all subject areas and assessment types. Over 21,000 academic institutions, publishers, and corporations use our services: Feedback Studio, Originality, Gradescope, ExamSoft, Similarity, and iThenticate.
Experience a remote-centric culture that empowers you to work with purpose and accountability in a way that best suits you, supported by a comprehensive package that prioritizes your overall well-being. Our diverse community of colleagues are all unified by a shared desire to make a difference in education.
Turnitin is a global organization with team members in over 35 countries including the United States, Mexico, United Kingdom, Australia, Japan, India, and the Philippines.
Job Description
AI and data science are integral to our success and ambitious product roadmap, and great AI begins with great data. Joining us as an AI Data Engineering Manager means you’ll become part of a global team of proactive, supportive, and independent professionals committed to delivering sophisticated, well-structured AI and data systems. You’ll help pioneer our next generation data and AI pipelines to scale our team’s impact. Additionally, you’ll collaborate with different teams within Turnitin to integrate AI and data science across a broad suite of products, designed to enhance learning, teaching, and academic integrity.

REQUIRED QUALIFICATIONS:

• At least 5 years of experience in data engineering and data science, ideally focused on enabling and accelerating AI R&D.
• At least 2 years of experience in a managerial or technical leadership role, with responsibility covering large cross-functional projects.
• Strong proficiency in Python, SQL, and Infrastructure as Code (Terraform, CloudFormation), with additional experience in modern orchestration frameworks (Airflow, Prefect, or dbt).
• Proficiency with cloud-native data platforms (AWS, Azure, GCP) and vector databases (Pinecone, Weaviate, Qdrant, or Chroma).
• Experience with MLOps tools and platforms (HuggingFace, SageMaker Bedrock, Vertex AI), experiment tracking (MLflow, Weights & Biases), and model deployment pipelines.
• Experience with Large Language Models (LLMs), embedding generation, retrieval-augmented generation (RAG) systems, and frameworks for orchestrating LLM interaction (LiteLLM, LangFuse, LangChain, LlamaIndex).
• Strong problem-solving, analytical, and communication skills, with the ability to design scalable AI data systems and collaborate effectively in cross-functional teams.

DESIRED QUALIFICATIONS:

• 7+ years of experience in data engineering and data science, with a focus on AI and machine learning projects.
• 2+ years of experience in a managerial or technical leadership role.
• Experience in education, EdTech, or academic integrity sectors.
• Experience using AI coding tools (Cursor, Claude Code, GitHub Copilot) for accelerated development.
• Familiarity with natural language processing, computer vision, or multimodal AI applications.
• Experience with data visualization (Streamlit) and data reporting.

In your role as AI Data Engineering Manager, you’ll lead a team that owns all data collection and data curation for the AI team. There will be a particular emphasis on automated data collection and labeling using state of the art AI and LLMs. Creativity, collaboration, and a strong problem solving mindset will be critical to your success. The critical responsibilities of the AI Data Engineering Manager will include:

• Leadership: Build and grow a team of AI data engineers, ensuring their growth and high performance.
• Strategy: Serve as a thought leader in data engineering, advising senior leadership on how to leverage AI-driven data engineering to create future-ready data and AI strategies.
• Communication: Ensure clarity of the company’s vision and mission across the team and foster excellent communication within the organization.
• AI Data Engineering: Design, build, operate and deploy real-time data pipelines at scale using AI methods and best practices. Apply cutting edge data warehousing, data science and data engineering technologies to accelerate Turnitin’s AI R&D efforts. Identify strategic unlocks such as LLM agents to enable a faster time-to-market and better reusability of new AI initiatives.
• Collaboration: Cross-functionally partner with teams from across Turnitin and especially the AI R&D, Applied AI, and Data Platform teams to collect, create, curate and catalog high-quality AI datasets that drive our AI pipeline and help answer critical business questions. Ensure alignment and integration of data architecture and data models across different products and platforms
• Hands-on Involvement: Engage in data engineering and data science tasks as required to support the team. Lead external data collection efforts - including state of the art prompt engineering techniques - to support the construction of state of the art AI models.
• Innovation: Drive data innovation through research and development to unearth insights from Turnitin’s rich data resources.
• Continuous Learning: Stay updated on new tools and development strategies in a rapidly evolving technical space, and bring innovation recommendations to leadership.

Qualifications