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

📋 Description

• Design, create, and implement machine learning infrastructure to tackle intricate marketplace challenges

• Enhance the ML training and serving platform in collaboration with the organization-wide ML Foundation team

• Optimize training and serving constraints concerning throughput, cost, latency, GPU/accelerator utilization, and tail latency

• Detect system and operational instabilities to boost availability

• Construct real-time feature-processing infrastructure and low-latency pipelines for up-to-date model features

• Spearhead infrastructure for a real-time sequential foundation model, including online sequence feature stores, streaming updates, long-context serving, and training pipelines

• Work alongside product managers, data scientists, and machine learning engineers to collaboratively design systems

• Engage with stakeholders to ensure solutions align with business objectives

• Mentor engineers, establish technical direction, and perform code reviews

• Promote ongoing improvement and technical excellence

⛳️ Requirements

• Graduate degree (master's or PhD) in computer science, artificial intelligence, machine learning, operations research, or equivalent self-study and experience

• Over 3 years of industry experience in developing ML infrastructure or applying machine learning to address real-world problems with substantial datasets

• Profound expertise in building, scaling, and managing ML infrastructure for training and/or serving in production environments

• Solid understanding of contemporary machine learning systems and MLOps practices

• Strong programming abilities in Python and/or Go

• Proficiency in SQL, Spark, and Pandas

• Familiarity with Torch, TensorFlow, and XGBoost

• Excellent analytical and problem-solving capabilities

• Outstanding verbal and written communication skills

• Capacity to collaborate with diverse stakeholders at all levels

• Openness to working in new and unfamiliar domains, languages, and problem areas