About the job
At Turnitin, Machine Learning is at the heart of our innovative strategies for success. We have an ambitious product roadmap, and we're looking for a Principal Machine Learning Scientist to join our global team of inquisitive and dedicated scientists and engineers. Together, we aim to develop state-of-the-art Machine Learning systems that seamlessly integrate into our extensive range of learning, teaching, and integrity products.
Your role will have a significant global impact, as our solutions are used by countless instructors teaching millions of students worldwide. With billions of submissions processed through the Turnitin platform and millions of assessments graded on Gradescope and Examsoft, your work will contribute to our AI Writing detection system, automate feedback on student writing, and enhance the assessment creation and grading processes, among other critical backend functions.
Key Responsibilities:
As part of our applied science group with a strong focus on modern Deep Learning techniques, you will be expected to have a balanced skill set that encompasses both the scientific and software engineering aspects of (Deep) Machine Learning. Your primary focus will be on crafting innovative and deployable ML models and solutions where standard solutions may not exist. A strong grasp of the mathematics behind machine learning and deep neural networks is essential for constructing novel model architectures, loss functions, training methods, and training loops. Keeping up with the latest research advancements in AI and Deep Learning is crucial, as you will apply these insights to your work. While we utilize established training platforms, we also develop our own training processes. Furthermore, the models you create must be deployable within our products, necessitating proficiency in production-level coding and software engineering. You will handle large models, sometimes up to hundreds of billions of parameters, requiring expertise in training across multiple GPUs and nodes, along with knowledge of cutting-edge training and inference techniques. Additionally, your models must excel in production environments, balancing accuracy with computational cost. A solid background in Computer Science will be vital as you engage in dataset exploration, generation (including synthetic datasets), design, construction, and analysis, often dealing with large datasets that require efficient parallel processing pipelines. You will also have opportunities to develop and present demos, as well as publish your findings in reputable peer-reviewed journals.
