Senior Machine Learning Scientist - Applied Research (USA Remote)

Job Description

Job Description

Company Description

When you join Turnitin, you'll be welcomed into a company that is a recognized innovator in the global education space. For over 25 years, Turnitin has partnered with educational institutions to promote honesty, consistency, and fairness across all subject areas and assessment types. Over 21,000 academic institutions, publishers, and corporations use our services: Feedback Studio, Originality, Gradescope, ExamSoft, Similarity, and iThenticate.

Experience a remote-centric 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. Our diverse community of colleagues are all unified by a shared desire to make a difference in education.

Turnitin is a global organization with team members in over 35 countries including the United States, Mexico, United Kingdom, Australia, Japan, India, and the Philippines.

Turnitin, LLC is an equal opportunity employer- vets/disabled.

Job Description

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

We’re an applied science group leaning towards modern Deep Learning. We expect our Senior Machine Learning Scientists to have a well-balanced set of skills, both in the Science as well as Software Engineering aspects of (Deep) Machine Learning. You will focus on developing novel and deployable ML models and solutions where no ready-made solution may be available. Therefore you need to be conversant enough with the mathematics of machine learning and deep neural networks such that you can construct novel model architectures, loss functions, training methods, training loops etc. You are also expected to keep abreast of the latest research advancements in AI and Deep Learning across modalities and apply those to your work. While we leverage ready-made training platforms, we also write our own training loops. Additionally, the models need to be directly deployable in our products, therefore, production level coding and software engineering proficiency is required. You may train large models (up to 100s of billions of parameters) therefore, ability to train on multiple GPUs and nodes and knowledge of the latest model training and inferencing advancements is necessary. Next, the models must perform well in production not only in terms of accuracy but also compute-cost. Delivering such software requires a sufficiently deep Computer Science background. Dataset exploration, generation (synthetic), design, construction and analysis, are a routine part of the job and may occupy a significant fraction of your time. Also, datasets can be large (billions of samples), therefore the ability to write parallel and efficient pipelines is a necessary skill. You will also be involved in code & model maintenance, code hardening (preparing the model and code for production pipelines), developing and staging demos and presenting your work within the company as well as via publications in peer reviewed venues (preferably A/A+ rated).

Day-to-day, your responsibilities are to:

• 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.

Qualifications

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 (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.

Would be a plus:

• 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).

Additional Information

The expected annual base salary range for this position is: $111,000/year to $185,000/year. This position is bonus eligible / commission-based.

As a Remote-First company, actual compensation will be provided in writing at the time of offer, if extended, and is determined by work location and a range of other relevant factors, including but not limited to: experience, skills, degrees, licensures, certifications, and other job-related factors. Internal equity, market and organizational factors are also considered.

Total Rewards @ Turnitin
Turnitin maintains a Total Rewards package that is competitive within the local job market. People tend to think about their Total Rewards monetarily — solely as regular pay plus bonus or commission. This is what they earn in exchange for what they do. However, Turnitin delivers more than just these components. Beyond the intrinsic rewards of unleashing your potential to positively impact global education, and thriving in an organization that is free of politics and full of humble, inclusive and collaborative teammates, the extrinsic rewards at Turnitin include generous time off and health and wellness programs that offer choice and flexibility and provide a safety net for the challenges that life presents from time to time. Experience a remote-centric 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.

Our Mission is to ensure the integrity of global education and meaningfully improve learning outcomes.

Our Values underpin everything we do.

• Customer Centric - We realize our mission to ensure integrity and improve learning outcomes by putting educators and learners at the center of everything we do.
• Passion for Learning - We seek out teammates that are constantly learning and growing and build a workplace which enables them to do so.
• Integrity - We believe integrity is the heartbeat of Turnitin. It shapes our products, the way we treat each other, and how we work with our customers and vendors.
• Action & Ownership - We have a bias toward action and empower teammates to make decisions.
• One Team - We strive to break down silos, collaborate effectively, and celebrate each other’s successes.
• Global Mindset - We respect local cultures and embrace diversity. We think globally and act locally to maximize our impact on education.

Global 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*

* varies by country

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 evolve alongside us, join our team!

Turnitin, LLC is committed to the policy that all persons have equal access to its programs, facilities and employment. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.