Adhita Selvaraj
About Adhita Selvaraj
Adhita Selvaraj is a Senior Software Engineer with extensive experience in machine learning infrastructure and Kubernetes. He has contributed to projects such as Kubeflow and dl-kops, and has worked for notable companies including Cisco, Viaduct, and Narvar.
Work at Narvar
Adhita Selvaraj currently holds the position of Senior Software Engineer at Narvar, having joined the company in 2023. This role is based in the San Francisco Bay Area and is conducted remotely. In this capacity, Selvaraj applies expertise in software engineering to contribute to the development of solutions that enhance customer experiences in the post-purchase phase.
Previous Experience at Cisco Meraki
Before joining Narvar, Selvaraj worked as an ML Infrastructure Engineer at Cisco Meraki from 2020 to 2021 for a duration of five months. During this time, she contributed to the development of Kubeflow, a machine learning toolkit tailored for Kubernetes. Her work included developing features for dl-kops, enabling single-click installations of Kubernetes along with CUDA and Nvidia Drivers.
Background in Software Engineering
Adhita Selvaraj has a diverse background in software engineering, having worked at various companies. She served as a Software Engineer (Infra/ML) at Viaduct from 2021 to 2022 and as a Senior Backend Engineer for the ML Platform at Nomad Health from 2022 to 2023. Her experience includes working on the kube-arbitrator project within the Kubernetes incubator, focusing on machine learning job scheduling.
Education and Expertise
Selvaraj earned her Master’s Degree in Electrical Engineering from Stony Brook University, where she studied from 2016 to 2018. Prior to that, she completed her Bachelor of Engineering in Electronics and Communication at Anna University Chennai from 2012 to 2016. She possesses programming expertise in languages such as Go, Python, and Swift, which she applies in her engineering roles.
Contributions to Machine Learning Initiatives
Adhita Selvaraj has actively participated in significant machine learning initiatives. She is involved in the MLPerf inference working group, which benchmarks machine learning performance. Additionally, she has conducted research on a Kubernetes scheduler aimed at ensuring fair allocation of GPU resources, further demonstrating her commitment to advancing machine learning infrastructure.