Full-Time Senior Machine Learning Scientist - Applied Research

Machine Learning is integral to the continued success of our company. You will join a global team to deliver cutting-edge, well-engineered Machine Learning systems. You will work closely with product and engineering teams to integrate Machine Learning into a broad suite of learning, teaching and integrity products.

Responsibilities and Requirements
You will focus on developing novel and deployable ML models and solutions where no ready-made solution may be available. You need to be conversant 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. Production level coding and software engineering proficiency is required. Ability to train on multiple GPUs and nodes and knowledge of the latest model training and inferencing advancements is necessary. Dataset exploration, generation (synthetic), design, construction and analysis, are a routine part of the job.

Day-to-day, your responsibilities are to:

• Research and develop production grade Machine Learning models.
• Optimize models for scaled production usage.
• Work with colleagues 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.
• 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.
• 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.
• Work with downstream teams to productionize your work.
• Communicate insights, as well as the behavior and limitations of models, to peers, subject matter experts, and product owners.
• Present and publish your work.

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