Mrityunjay Kumar Email : mjay.cse@gmail.com

¯/mj2030 </mrityunjaykumar911 Mobile : +1-631-710-1058

Education

• Stony Brook University New York, USA

Degree: Master of Science in Computer Science Jan-2019 – May-2020

• Maulana Azad National Institute Of Technology Bhopal, India

Degree: Bachelor in Technology with major in Computer Science & Engineering July-2010 – April-2014

Programming Skills

• Programming Languages: C++, C, Java, Golang, JavaScript, SQL

• Technologies: Apache Storm, Apache Spark, Redis, REST,Apache Kafka, AWS, Git/Perforce, MATLAB

• Relevant Coursework: Distributed Systems, Operating Systems, Analysis of algorithms, Visualization

Experience

• Microsoft May’2022 – till

Software Engineer-2 Mountain View, CA

◦ Snap Service

∗ Implemented new lightweight service layer to provide native renderer workers to download from Storage service

to Azure blob cache to improve streaming latency by 25%.

∗ Implemented testing framework to improve service reliability score by 85% and improved deployment

frequency by 10%.

• VMware Inc. July’2020 – May’2022

Member of Technical Staﬀ-III Palo Alto, CA

◦ Virtual distributed ﬁle system, Storage layer

∗ Conceptualized and implemented distributed FSCK tool for analyzing and repairing storage metadata. This

tool is leveraged to analyze the state of metadata storage on disk after crash recovery by forming analysis

matrices and cross consistency patterns between multiple metadata data structures like B+ trees, bitmap,

segment usage table

∗ End-to-end implementation of a cloud-native microservice to validate the integrity of the ﬁle system k-v store

(such as B+ trees). The service can be scheduled or run on-demand to check the health of the storage system

k-v store. In addition, the service is also used for other storage features, such as snapshots, un-map, segment

cleaning to verify consistency and integrity of the metadata.

∗ Implemented aggregated snapshot capacity from scratch to support statistics collection with p99 latency in

range of 30 ms.

• Distributed Systems Lab - Stony Book University June’2019 – May’2020

Graduate Research Assistant, Prof. Shuai Mu Stony Brook, NY

◦ Distributed Multi-core Transactional Database Engine

∗ Implemented asynchronous replication with multi-process paxos to achieve minimal loss in throughput

∗ Designed multi-core log truncation mechanism to support check-pointing for the transaction recovery protocol,

which allowed read only transactions to scale 10M ops/sec.

∗ Implemented replay protocol to guarantee serializibility and consistency check pipeline for in-memory

streams & disk logs.

∗ Implemented veriﬁcation pipeline for generating CPU/Heap/Memory throttling graph using gperftools &

mutrace tool.

∗ Implemented fast header-only/compiled, C++ logging library facilitating aligned memory allocators with

features like Rotating log and auto-ﬂush to make transparent transaction serialization

• Talentica Software April’2016 – Jan’2019

Senior Software Engineer Pune, India

◦ Estimation & Prediction algorithms for Wireless Systems

∗ Designed & Implemented a stream data pipeline for machine learning inferences & predictions using Apache

Storm, Apache Kafka, AWS, Cassandra.

∗ Improved network traﬃc prediction accuracy to 80% using important native SLA metrics, RF Frequencies &

feedback loop from auxiliary methods.

∗ Implemented feedback pipeline to improve accuracy of indoor location prediction algorithm by 95% for live

BLE assets.

∗ Developed a multi-modal Machine Learning model for network traﬃc classiﬁcation for Audio and Video

streaming using Ensemble of regression and auto-encoders.

∗ Added an ad-hoc client to Storm topology to support collection of Real-time data to Disk & Batch pipeline

using Java, Apache Spark.

∗ Developed a live BLE asset view portal to support data collection team, adding 70% more correctness to site

calibration using JavaScript, Apache Kafka, Python.

◦ Machine Learning Algorithm object storage service

∗ Designed continuous delivery pipeline for Machine Learning models using gRPC, protobuf, Redis, RabbitMQ,

AWS S3 improving deployment frequency by 40%.

∗ Wrote polyglot client suite in Java, python, Go, C++ to facilitate generic object serialization which lead to

improved continuous deployment frequency.

◦ File Sync Application

∗ Implemented delta ﬁle sync Service in C++ using native git-tree diﬀ algorithm which reduce load in sync server

by 400%.

∗ Developed an update framework to support release notiﬁcation & binary upgrade for multi-talent architecture

using Java, C++, Python.

∗ Designed rate limiter service to prevent throttling of indexing layer & sync layer, improving average P99 sync

latency by 30%.

◦ Financial Document Search Engine:

∗ Improved search relevancy by 45%. Improved Online Ontology enhancer by 30% via optimizing Neo4j query

consumption, NLP Pipeline for Document Clustering, Keyword Extraction, Text Classiﬁcation.

• MediaTek Aug’2014 – April’2016

Software Engineer Noida, India

◦ As a feature owner of Audio module, implemented design and bug-ﬁxes for new product release. Improved

playlist design to reduce playlist update overload by 25%, Integrated BT Stack in MMI Layer

◦ Wrote Software Layer code for Stress Test based Combo GPS-WiFi-BT Tool in VC++

Projects

• Raft - A distributed fault tolerant in-memory key value database

- Implemented Sharded and replicated fault-tolerance key-value store based on raft protocol from scratch in Go.

- Tweaked leader’s election to ensure progress in case of repetitive failure in re-election step

• Map-Reduce library Implemented distributed map reduce library and worker failures from paper.

• Backup File System in Linux Kernel Implemented stackable virtual ﬁle system with Custom Visibility,

Retention and Version Management Policy to support backup to ﬂat ﬁles under guidance of Prof. Erez Zadok using C

and Linux VFS Layer.

• Encryption based System tool for Linux Kernel Implemented system call for encrypt/decrypt ﬁle using AES

algorithm provided by Linux kernel crypto API under guidance of Prof. Erez Zadok in C.

Publications

• Learning to Fingerprint the Latent Structure in Question Articulation: In this paper, we show that the latent

patterns of questions can be represented as a system that maximizes a cost function related to the underlying objective

and can be approximated to building a memory of patterns represented as a trained neural auto-encoder. [publication]