This is a fully remote position, open to applicants in California.

📋 Description

• Investigate novel reasoning algorithms and models

• Create processes and tools for model benchmarking

• Design efficient and effective ML data pipelines

• Modify frameworks and interfaces to speed up machine learning development

• Establish the infrastructure for data augmentation pipelines and synthetic data generation

• Work collaboratively with other teams to identify their challenges and priorities, defining milestones for the corresponding roadmaps

• Formulate practical solutions and integrate them with the outcomes from other teams to deliver optimal results

⛳️ Requirements

• You possess an M.Sc. with a focus on one or more of the following disciplines: Computer Science, Artificial Intelligence, Mathematics, or a related field

• Over 3 years of hands-on experience in ML Infrastructure, DataOps, and distributed training

• Proficiency in programming languages and tools essential for high-performance computing, particularly in Python/C++, along with machine learning frameworks such as PyTorch, TensorFlow, or JAX

• Capability to comprehend deep learning algorithms, particularly in areas like natural language processing and reasoning

• Familiarity with cloud services from Azure, AWS, or GCP for MLOps and DataOps pipelines

• Expertise in managing Kubernetes clusters and distributed computing resources

• Excellent communication and teamwork abilities

• Willingness to explore and advocate for innovative technologies in the ML Infrastructure sector and beyond