Lead the design, development, and deployment of advanced machine learning models to enhance system performance and scalability.
Tackle complex challenges associated with resource-intensive models using distributed systems and parallel computing.
Advance methodologies for controlling, monitoring, and analyzing machine learning models in production environments.
Develop new approaches to adversarial testing, model evaluation, and robust inference.
Translate research ideas into scalable AI systems deployed in real-world, adversarial settings.
Work closely with cross-functional teams to ensure research outcomes inform production systems.

Bachelor’s degree in Computer Science, Machine Learning, Engineering, or a related technical field is required.
Experience in building and deploying machine learning models and systems.
Demonstrated expertise in designing, training, and deploying deep learning models with frameworks like PyTorch.
Strong programming experience in Python and C++ (preferred)
Practical experience developing scalable machine learning pipelines and integrating them with cloud infrastructure (e.g., AWS, GCP, Azure).
Experience conducting ML research, including building research prototype systems, experiment design, empirical analysis of results, and communicating results via publications.
Good to have: experience with modern ML methods such as LLMs (training, finetuning, and/or analyzing), synthetic data generation pipelines, and AI safety or security work.

Machine Learning Engineer

Key skills