Full-Time Principal Machine Learning Scientist

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

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

Qualifications

• Experience working with text data to build Deep Learning and ML models, both supervised and unsupervised. Experience with deep learning in other modalities such as vision and speech would be a strong bonus.
• A strong understanding of the math and theory behind machine learning and deep learning.
• Software engineering background with at least 3-5 years of experience (we use Python, SQL, Unix-based systems, git, and github for collaboration and review).
• Machine / Deep Learning development skills, including experiment tracking (we use AWS SageMaker, Hugging Face, transformers, PyTorch, scikit-learn, Jupyter, Weights & Biases).
• An understanding of Language Models, using and training / fine-tuning and a familiarity with industry-standard LM families.
• Master's degree or PhD in Computer Science, Electrical Engineering, AI, Machine Learning, applied math or related field, with relevant industry experience, or outstanding previous achievements in this role. A Computer Science background is required as opposed to statistics or pure mathematics. We’re an applied science group leaning towards deep learning and therefore software development proficiency is a prerequisite.
• Excellent communication and teamwork skills.
• Fluent in written and spoken English.

Would be a plus

• Familiarity in coding for at-scale production, ranging from best practices to building back-end API services or stand-alone libraries.
• Essential dev-ops skills (we use Docker, AWS EC2/Batch/Lambda).
• Familiarity in building front-ends (LLMs or more standard React, Javascript, Flask) for simple demos, POCs and prototypes.
• Experience with advanced prompting, fine-tuning or training an LLM, open-source or cloud, using industry accepted platforms (such as mosaic.ai or stochastic.ai).
• Showcase previous work (e.g. via a website, presentation, open source code).

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