Shape the future of AI in education by building intelligent systems that support learning, teaching, and academic integrity at global scale. As a Senior Machine Learning Scientist, you'll lead the development of robust, high-performance models grounded in cutting-edge research and real-world application needs.
What You’ll Do
- Design, train, and refine novel deep learning architectures, including large-scale models with hundreds of billions of parameters
- Develop efficient, parallel data pipelines to process massive datasets, ensuring quality and scalability
- Apply and adapt foundation models—fine-tuning locally hosted LMs or leveraging APIs through prompt engineering and agent-based workflows
- Collaborate with engineers, product teams, and domain experts to translate challenges into effective ML-driven solutions
- Diagnose and improve model behavior in production, balancing accuracy, efficiency, and computational cost
- Contribute to responsible AI practices by curating datasets and maintaining models with ethical considerations
- Stay ahead of advancements by reviewing research, experimenting with new techniques, and sharing insights across teams
- Write modular, testable code and help guide its integration into product releases
- Communicate technical findings clearly to both technical and non-technical stakeholders
- Present and, where appropriate, publish work to advance organizational knowledge
What We’re Looking For
- Strong command of both machine learning theory and software engineering discipline
- Proven ability to build and train custom model architectures, loss functions, and training loops
- Deep understanding of the mathematical foundations of neural networks and optimization
- Experience with distributed training across multiple GPUs and compute nodes
- Proficiency in handling diverse data sources—SQL, no-SQL, and web-scale datasets
- Familiarity with modern inferencing techniques and model deployment strategies
- A track record of delivering high-accuracy, low-latency models in production environments
Preferred Background
- History of presenting technical work internally or at peer-reviewed conferences (A/A+ venues preferred)
- Active engagement with the research community through reading, experimentation, or publication
Technology Environment
You’ll work with deep learning frameworks, large language models, prompt engineering tools, agent systems, and scalable data infrastructure. Automated testing, version-controlled pipelines, and reproducible research practices are core to our workflow.
Work Environment
This is a fully remote position for candidates based in the United States, with flexibility to support asynchronous collaboration across global time zones. We value independence, intellectual curiosity, and a shared commitment to building well-engineered, impactful AI systems.
