My resume for opportunities in 2024

1. Dilnoza Bobokalonova
SOFTWARE ENGiNEER · BACKEND & EMBEDDED SYSTEMS
 917.592.2825 |  dilnoza1@berkeley.edu |  tinyurl.com/DilNova |  dilnozabobokalonova1 |  dilnozabobokalonova1
Summary
I thrive in challenging environments and am deeply passionate about learning and engineering critical systems. Over the past 8 months, I’ve
worked on acquiring skills in embedded systems, exploring space technology, and developing whitehat projects in Rust. My background is in
building Deep Learning & NLP models at Berkeley, implementing robust backend software systems at Coursera, and leveraging Rust in various
contexts. I am now in search of my next opportunity to advance and grow as an engineer and leader in the Space industry.
Education
University of California, Berkeley Berkeley, CA
MASTER OF ENGiNEERiNG iN ELECTRiCAL ENGiNEERiNG AND COMPUTER SCiENCE | DATA SCiENCE & SYSTEMS Aug. 2018 ‑ May 2019
University of Miami Coral Gables, FL
B.S. iN COMPUTER SCiENCE | MiNORS iN MATHEMATiCS & INTERNATiONAL STUDiES Aug. 2014 ‑ May 2018
Skills
Programming/Scripting Rust, Scala, Embedded C, Java, Python, LISP, Prolog, Bash, MATLAB, LaTeX, Dart, JavaScript, HTML/CSS
Dev, ML & Technologies Jenkins, Docker, ZooKeeper, Tensorflow, NLTK, Pandas, DynamoDB, ElasticSearch, KiCAD, Terraform, AWS
Languages Russian, Tajik, English
Work Experience
Coursera Mountain View, CA
SOFTWARE ENGiNEER Jun. 2019 ‑ May. 2023
• Engineered and maintained resilient backend systems meeting strict SLA requirements (99.999% uptime) using Scala (REST) and Java (gRPC),
enhancing service reliability and scalability with various deployment and monitoring tools. (ZooKeeper, SumoLogic, Jenkins, DataDog)
• Authored system design documents for 8 high‑profile projects, designing their architecture and securing consensus across engineering, legal,
product, and enterprise teams, accelerating launch timelines by 20% and navigating early around implementation bottlenecks.
• Directed the LevelSets project (2021‑2022) as Lead Engineer, architecting it as a standalone app. Addressed cross‑functional team requirements
and launched the app platform‑wide, directly contributing to a 30% increase in revenue.
• Optimized and refactored Core Learner legacy code, cutting error rates by 35%, resolving bottlenecks, and eliminating timeout errors; enhanced
system reliability and resource utilization, resulting in a 5% reduction of monitoring costs.
• Drove a successful transition of backend services from Scala to Java and upgraded APIs from RESTful to gRPC, unifying complex business logic
across services & achieving a 40% boost in performance and a 2x increase in system scalability.
• Reorganized on‑call rotations for Engineering during a critical team split in 2021; initiated a strategic meeting to redesign schedules & address
engineers’ concerns, increasing scheduling efficiency by 50% and ensuring a smooth reorganization. (PagerDuty, SumoLogic, DataDog)
• Extensively collaborated with the Data Science team to integrate ML models with Learner & Skills BE services (AWS SageMaker, AWS Lambda,
Scala Naptime); eliminated engineering bottlenecks between engineering & DS teams, accelerating project timelines by 20%.
• Streamlined engineering onboarding by leading backend sessions, halving new hires’ integration time with targeted training on Coursera’s stack
and dev pipeline, enhancing early team contribution. Leveraged Confluence for training material distribution.
• Mentored multiple junior engineers and interns, encouraging creative freedom in their projects while ensuring alignment with team culture and
performance standards, consistently achieving smooth team integration and accelerated project completion times.
UC Berkeley Coleman Fung Institute for Engineering Leadership Berkeley, CA
DATA SCiENTiST & NATURAL LANGUAGE PROCESSiNG DEVELOPER Jun. 2018 ‑ May. 2019
• Utilized powerful natural language processing and machine learning techniques to analyze the technology development of autonomous vehi‑
cles (AV) industry, specifically LIDAR technology.
• Implemented document similarity analysis to expand 1 patent seed to a pool of 1000 similar patents drawn from the AV data of 40000 patents.
• Developed various models such as SVM, Random Forest, Long Short‑Term‑Memory neural network, to forecast the quantity and spatial distri‑
bution of future patents across 244 distinct CPC classes for the 2019‑2020 quarters, achieving an accuracy rate of 96.1%.
• Performed dimensionality reduction (PCA) to convert an original 33k‑feature vector to 3D and visualize the future patent space of LIDAR in VR.
Projects
Rust BCL (Berkeley Container Library) Berkeley National Laboratory, CA
RUST iN DiSTRiBUTED COMPUTiNG USiNG OPEN MPI | CORi SUPERCOMPUTER Jan. 2019 ‑ May. 2019
• Developed the Rust Berkeley Container Library (RBCL) designed specifically for high‑performance distributed computing environments, lever‑
aging the power and scalability of the Cori supercomputer at the Lawrence Berkeley National Laboratory.
• Engineered RBCL infrastructure with MPI and advanced memory management, achieving a 30% improvement in data sharing efficiency and
reducing synchronization overhead by 25% across distributed nodes.
• Conducted scalability tests on RBCL over Cori’s processor nodes, optimizing communication and load balancing to support efficient scaling up
to 500+ nodes with a 20% reduction in overhead.
• Benchmarked RBCL under various parallelism levels and cluster sizes, demonstrating a 2x throughput increase and a 50% latency reduction,
while maintaining optimal resource utilization up to 80% efficiency, markedly outperforming the C++ implementation.
FEBRUARY 12, 2024 DiLNOZA BOBOKALONOVA · RÉSUMÉ 1

2. WhiteHat & Rust Notion | GitHub
ASYNC, THREAD CONCURRENCY, PORTS DiSCOVERY & EXPLOiTS Jan. 2023 ‑ PRESENT
• Developed a Rust‑based scanner using multi‑threading to identify vulnerable open ports across specified subdomains and IP addresses, im‑
proving discovery speed by 3x compared to traditional methods.
• Upgraded the multi‑threaded scanner with Tokio for asynchronous execution, reducing context switching latency by 8.5x and enhancing scan
efficiency by 40%.
• Developed an advanced concurrent web crawler in Rust leveraging asynchronous programming, atomic operations, and regex to efficiently
scrape GitHub Organization users, JavaScript web applications, and extract CVE data.
• Integrated Tokio’s MPMC channels for enhanced parallel task execution and designed dynamic concurrency adjustment to optimize perfor‑
mance and minimize resource contention.
• Incorporated fault‑tolerant mechanisms such as error handling and backoff strategies to gracefully handle network failures.
• Transformed a Python exploit into Rust, targeting mirror repository URL vulnerabilities (CVE‑2019‑11229).
Embedded Systems Engineering University of California, San Diego
HARDWARE DESiGN, ARM, STM32, HAL, BSP & BAREMETAL | FPGA XiLiNX ZYNQ‑7000 Jun. 2023 ‑ PRESENT
• Developed bare‑metal code for STM32F3 drivers including I2C, SPI, UART, GPIO, Timer, and Systick.
• Implemented interrupt‑driven programming for UART, GPIO, ADC, Systick, and Timer along with DMA for optimized data handling.
• Soldered an 8‑pin connector to the FRAM PCBA (MB85RS64V) for integration with the IoT board’s SPI Interface, effectively isolating the FRAM
module’s communication channel from other peripherals.
• Created an embedded system hardware design for a high‑speed STM32L4 microcontroller in KiCAD; conducted verification tests of sensors and
produced detailed schematics, BOM, and Netlist.
Deep Learning Specialization deeplearning.ai
TENSORFLOW, NLTK, PANDAS | 12 PROJECTS TOTAL Aug. 2017 ‑ May. 2018
• Developed a car detection algorithm for autonomous driving using You Only Look Once (YOLO) model containing over 50 million parameters
able to detect 80 different classes in an image.
• Created a face recognition system to map face images into 128‑dimensional encodings for accurate element‑wise comparison.
• Built a Neural Machine Translation model to translate human readable dates into machine‑readable dates by using a sequence‑to‑sequence
model.
• Synthesized & processed audio recordings to create a dataset used to implement an algorithm for trigger word detection.
FEBRUARY 12, 2024 DiLNOZA BOBOKALONOVA · RÉSUMÉ 2