Machine Learning is integral to the continued success of our company. Our product roadmap is exciting and ambitious. You will join a global team of curious, helpful, and independent scientists and engineers, united by a commitment to deliver cutting-edge, well-engineered Machine Learning systems. You will work closely with product and engineering teams across Turnitin to integrate Machine Learning into a broad suite of learning, teaching and integrity products.

We are in a unique position to deliver Machine Learning used by hundreds of thousands of instructors teaching millions of students around the world. Your contributions will have global reach and scale. Billions of papers have been submitted to the Turnitin platform, and hundreds of millions of answers have been graded on the Gradescope and Examsoft platforms. Machine Learning powers our AI Writing detection system, gives automated feedback on student writing, investigates authorship of student writing, revolutionizes the creation and grading of assessments, and plays a critical role in many back-end processes.

Responsibilities and Requirements

We expect Senior Machine Learning Scientists to be versatile and have a well-balanced set of skills. You will focus on model training, with significant capacity for research (developing novel model architectures), dataset construction, and model hardening (preparing the model and code for production pipelines).

Day-to-day, your responsibilities are to:

• Work with subject matter experts and product owners to determine what questions should be asked and what questions can be answered.
• Work with subject matter experts to curate, generate, and annotate data, and create optimal datasets following responsible data collection and model maintenance practices.
• Answer questions and make trainable datasets from raw data, using efficient SQL queries and scripting languages, visualizing when necessary.
• Develop and tune Machine Learning models, following best practices to select datasets, architectures, and model parameters.
• Utilize, adopt, and fine-tune Language Models, including third-party LLMs (through prompt engineering and orchestration) and locally hosted LMs.
• Stay current in the field - read research papers, experiment with new models and LLMs, and share your findings.
• Optimize models for scaled production usage.
• Communicate data insights, as well as the behavior and limitations of models, to peers, subject matter experts, and product owners.
• Write clean, efficient, and modular code, with automated tests and appropriate documentation.
• Stay up to date with technology, make good technological choices, and be able to explain them to the organization.