NLP / LLM Scientist - Applied AI ML Lead - Machine Learning Centre of Excellence

The Machine Learning Center of Excellence invites the successful candidate to apply sophisticated machine learning methods to a wide variety of complex tasks including natural language processing, large language models, and recommendation systems. The candidate must excel in working in a highly collaborative environment together with the business, technologists, and control partners to deploy solutions into production. The candidate must also have a strong passion for machine learning and invest independent time towards learning, researching, and experimenting with new innovations in the field. The candidate must have solid expertise in Deep Learning with hands-on implementation experience and possess strong analytical thinking, a deep desire to learn, and be highly motivated.

Job Responsibilities

• Research and explore new machine learning methods through independent study, attending industry-leading conferences, experimentation, and participating in our knowledge-sharing community.
• Develop state-of-the-art machine learning models to solve real-world problems and apply them to tasks such as NLP, LLMs, or recommendation systems.
• Produce outputs that lead to high-impact business applications, open-source software, patents, and publications in top AI/ML conferences and journals.
• Collaborate with multiple partner teams such as Business, Technology, Product Management, Legal, Compliance, Strategy, and Business Management to deploy solutions into production.
• Drive firm-wide initiatives by developing large-scale frameworks to accelerate the application of machine learning models across different areas of the business.

Required Qualifications, Capabilities, and Skills

• Solid background in NLP and LLMs, and solid understanding of machine learning and deep learning methods.
• Published research in areas of Machine Learning, Deep Learning, or Reinforcement Learning at a major conference or journal.
• PhD in a quantitative discipline (e.g., Computer Science, Electrical Engineering, Mathematics, Operations Research, Optimization, or Data Science) with reasonable industry experience, or an MS with significant industry or research experience in the field.
• Extensive experience with machine learning and deep learning toolkits (e.g., TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas).
• Ability to design experiments and training frameworks, and to outline and evaluate intrinsic and extrinsic metrics for model performance aligned with business goals.
• Experience with big data and scalable model training and solid written and spoken communication to effectively communicate technical concepts and results to both technical and business audiences.
• Scientific thinking with the ability to invent and to work both independently and in highly collaborative team environments.
• Curious, hardworking, and detail-oriented, and motivated by complex analytical problems.

Preferred Qualifications, Capabilities, and Skills

• Strong background in Mathematics and Statistics and familiarity with the financial services industries and continuous integration models and unit test development.
• Knowledge in search/ranking, Reinforcement Learning, or Meta Learning.
• Expertise in recommendation systems.
• Experience with A/B experimentation and data/metric-driven product development, cloud-native deployment in a large-scale distributed environment, and ability to develop and debug production-quality code.

We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs.