Research Scientist Intern (AI Infrastructure)- 2026 Start (PhD)

About the role

The ideal candidate should be an expert in at least one of the following fields to define and design the next-gen AI Infrastructure: Infrastructure Design & Architecture, Performance Optimization, Distributed Systems & Scalability, Data Pipeline & Workflow Engineering.

Responsibilities

• Define and implement service-oriented, containerized architectures (Kubernetes, VM frameworks, unikernels) optimized for ML performance and security.
• Profile and optimize every layer of the ML stack—ML Compiler, GPU/TPU scheduling, NCCL/RDMA networking, data preprocessing, and training/inference frameworks.
• Develop low-overhead telemetry and benchmarking frameworks to identify and eliminate bottlenecks in distributed training and serving.
• Build and operate large-scale deployment and orchestration systems that auto-scale across multiple data centers (on-premises and cloud).
• Champion fault-tolerance, high availability, and cost-efficiency through smart resource management and workload placement.
• Architect and implement robust ETL and data ingestion pipelines (Spark/Beam/Dask/Flume) tailored for petabyte-scale ML datasets.
• Integrate experiment management and workflow orchestration tools (Airflow, Kubeflow, Metaflow) to streamline research-to-production.
• Partner with ML researchers to translate prototype requirements into production-grade systems.
• Mentor and coach engineers on best practices in performance tuning, systems design, and reliability engineering.

Qualifications

• Graduation date in 2026 year with a PhD in Computer Science, Engineering, or a related technical field.
• Understanding of infrastructure or systems engineering focused roles, with ML/AI infrastructure.
• Strong programming skills in Python, C++, Go, or Rust for systems development and automation.
• Excellent communicator able to bridge research and production teams.
• Strong problem-solving aptitude and a drive to push the state of the art in ML infrastructure.