Senior Machine Learning Scientist

Summary

Join Turnitin, a global leader in educational technology, as a Senior Machine Learning Scientist. You will contribute to the development and improvement of cutting-edge machine learning systems used by millions of students and instructors worldwide. This remote-first position offers a collaborative environment with a focus on innovation and impact. Responsibilities include model training, data curation, and collaboration with cross-functional teams. The ideal candidate possesses strong machine learning expertise, software engineering skills, and excellent communication abilities. Turnitin provides a comprehensive benefits package, including generous time off, health and wellness programs, and a remote-centric culture.

Requirements

• 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

Responsibilities

• 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 architectures and LLMs, and share your findings
• Optimize models for scaled production usage
• Communicate 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

Preferred Qualifications

• 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)

Benefits

• Remote First Culture
• Health Care Coverage*
• Education Reimbursement*
• Competitive Paid Time Off
• 4 Self-Care Days per year
• National Holidays*
• 2 Founder Days + Juneteenth Observed
• Paid Volunteer Time*
• Charitable contribution match*
• Monthly Wellness or Home Office Reimbursement/*
• Access to Modern Health (mental health platform)
• Parental Leave*
• Retirement Plan with match/contribution*