Senior Machine Learning Scientist - Applied Research; Remote

Why Consider This Opportunity

Salary up to $185,000/year. Bonus eligible position. Opportunity for career advancement and growth within a global organization. Flexible remote‑first work culture that empowers you to work with purpose. Comprehensive health and wellness programs, including education reimbursement and generous time off. Collaborative and inclusive work environment focused on making a positive impact in education.

• Research and develop production‑grade Machine Learning models optimized for scaled production usage.
• Collaborate with AI team, Engineering teams, and subject matter experts to explore product challenges and recommend innovative ML/AI solutions.
• Curate and generate optimal datasets, ensuring quality through responsible data collection and model maintenance practices.
• Investigate weaknesses of models in production and develop pragmatic solutions.
• Communicate insights and model behavior to peers and product owners, presenting findings in publications when applicable.

• Master’s degree or PhD in Computer Science, Electrical Engineering, AI, Machine Learning, applied math, or related field.
• At least 5 years of industry experience in Machine/Deep Learning and Software Engineering.
• Strong understanding of the math and theory behind machine learning and deep learning.
• Academic publications in peer‑reviewed conferences or journals related to Machine Learning.
• Proficiency in Machine/Deep Learning development skills, including experience with popular platforms like AWS Sage Maker and PyTorch.

• Experience working with text data to build Deep Learning and ML models, both supervised and unsupervised.
• Familiarity with building front‑ends for demos, POCs, and prototypes.
• Experience with advanced prompting/agentic systems and fine‑tuning or training a language model.
• Showcase of previous work through a website, presentation, or open‑source code.
• Essential dev‑ops skills, including knowledge of Docker and AWS services.

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