Senior Machine Learning Scientist - Applied Research (USA Remote)

Turnitin, LLC•Dallas, TX

35d•Remote

About The Position

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.

Requirements

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 (we use the python ecosystem for ML), 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 - preferably A/A+ rated such as NeurIPS, ICML, ICLR, AAAI, TMLR, JMLR, IJCAI, ICANN, KDD, ACL, EMNLP, NAACL, COLING, CVPR, ICCV, ECCV, IEEE etc.
Machine / Deep Learning development skills, including popular platforms (we use AWS SageMaker, Hugging Face, Transformers, PyTorch, PyTorch Lightning, Ray, scikit-learn, Jupyter, Weights & Biases etc.).
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.

Nice To Haves

We’re an applied science group, therefore Software development proficiency is a requirement. 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.
Familiarity in building front-ends (Gradio, Streamlit, Dash or more standard React, Javascript, Flask) for simple demos, POCs and prototypes.
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.
Essential dev-ops skills (we use Docker, AWS EC2/Batch/Lambda).

Responsibilities

Research and develop production grade Machine Learning models as described above. 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 (through prompt engineering and agents) 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.