Turnitin is looking to integrate Machine Learning into its suite of learning, teaching, and integrity products to enhance user experience and product capabilities, leveraging its vast dataset of student submissions.
Requirements
- 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.
- Experience with advanced prompting / agentic-systems and fine-tuning or training an LLM, using industry accepted platforms.
- 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).
- Ability to train on multiple GPUs and nodes and knowledge of the latest model training and inferencing advancements is necessary.
- Ability to write parallel and efficient pipelines is a necessary skill.
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.
- 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.
Other
- 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.
- 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.
- Excellent communication and teamwork skills.
- Fluent in written and spoken English.
