Senior Machine Learning Scientist - Applied Research (UK Remote)

When you join Turnitin, you'll be welcomed into a company that is a recognized innovator in the global education space. For more than 25 years, Turnitin has partnered with educational institutions to promote honesty, consistency, and fairness across all subject areas and assessment types. Turnitin products are used by educational institutions and certification and licensing programs to uphold integrity and increase learning performance, and by students and professionals to do their best, original work. Experience a remote-first 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.

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. Your contributions will have global reach and scale.

Responsibilities and Requirements:

• Research and develop production grade Machine Learning models.
• Optimize models for scaled production usage.
• Work with colleagues in the AI team, other Engineering teams, subject matter experts, Product Management, Marketing, Sales and Customer support to explore ongoing product issues, challenges and opportunities and then recommend innovative ML/AI based solutions.
• Help out with ad-hoc one-off tasks as a team player within the AI team.
• Work with subject matter experts to curate and generate optimal datasets following responsible data collection and model maintenance practices.
• Explore and access SQL, no-SQL and web data and write efficient parallel pipelines.
• Review and design datasets to ensure data quality.
• Investigate weaknesses of models in production and work on pragmatic solutions.
• Utilize, adopt, and fine-tune off the shelf models, including LLMs exposed via API and locally hosting LMs and other foundation models.
• Stay current in the field - read research papers, experiment with new architectures and LLMs, and share your findings.
• Write clean, efficient, and modular code with automated tests and appropriate documentation.
• Stay up to date with technology and platforms, make good technological choices, and be able to explain them to the organization.
• Work with downstream teams to productionize your work and ensure that it makes into a product release.
• Communicate insights, as well as the behavior and limitations of models, to peers, subject matter experts, and product owners.
• Present and publish your work.

Required Qualifications:

• Master's degree or PhD in Computer Science, Electrical Engineering, AI, Machine Learning, applied math or related field or outstanding previous achievements demonstrating excellence in Deep Machine Learning, Computer Science and Software Engineering.
• At least 5 years of industry experience in Machine / Deep Learning, Computer Science and Software Engineering.
• A strong understanding of the math and theory behind machine learning and deep learning is a prerequisite.
• Academic publications in peer reviewed conferences or journals related to Machine Learning.
• An understanding of Language Models, using and training / fine-tuning and a familiarity with industry-standard LM families.
• Excellent communication and teamwork skills. Fluent in written and spoken English.

Would be a plus:

• 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 Computer Science educational background is preferred as opposed to statistics or pure mathematics.
• Experience with advanced prompting / agentic-systems and fine-tuning or training an LLM, using industry accepted platforms.
• Showcase previous work (e.g. via a website, presentation, open source code).
• Familiarity in coding for at-scale production, ranging from best practices to building back-end API services or stand-alone libraries.

Total Rewards @ Turnitin:

At Turnitin, we believe Total Rewards go far beyond pay. While salary, bonus, or commission are important, they’re only part of the value you receive in exchange for your work. Beyond compensation, you’ll experience the intrinsic rewards of unleashing your potential and making a positive impact on global education. You’ll also thrive in a culture free of politics, surrounded by humble, inclusive, and collaborative teammates.

Our Mission is to ensure the integrity of global education and meaningfully improve learning outcomes. Our Values underpin everything we do:

• Customer Centric: Our mission is focused on improving learning outcomes; we do this by putting educators and learners at the center of everything we do.
• Passion for Learning: We are committed to our own learning and growth internally. And we support education and learning around the globe.
• Integrity: Integrity is the heartbeat of Turnitin—it is the core of our products, the way we treat each other, and how we work with our customers and vendors.
• Action & Ownership: We have a bias for action. We act like owners. We are willing to change even when it’s hard.
• One Team: We strive to break down silos, collaborate effectively, and celebrate each other's successes.
• Global Mindset: We consider different perspectives and celebrate diversity. We are one team. The work we do has an impact on the world.

Remote First Culture Health Care Coverage, Education Reimbursement, Competitive Paid Time Off, Self-Care Days, National Holidays, Charitable contribution match, Monthly Wellness or Home Office Reimbursement, Access to Modern Health (mental health platform), Retirement Plan with match/contribution.

Seeing Beyond the Job Ad: At Turnitin, we recognize it’s unrealistic for candidates to fulfill 100% of the criteria in a job ad. We encourage you to apply if you meet the majority of the requirements because we know that skills evolve over time. If you’re willing to learn and unleash your potential alongside us, join our team!