# Devin S. > LLMs @ Otter.ai, Machine Learning Engineer and Founder Location: San Francisco Bay Area, United States Profile: https://flows.cv/devins My primary focus is machine learning and applying it to different domains. I strive to build tools that augment human productivity, creativity, and capability. Feel free to reach out to me at dshah3@outlook.com. 🌐 https://dshah.dev/ With this, I'm pursuing a larger goal of engineering systems at the intersection of biology and AI. We can then, start to understand human consciousness one tensor and one neuron at a time. ## Work Experience ### Software Engineer II, LLM @ Otter.ai Jan 2025 – Present | Mountain View, California, United States ### Software Engineer, LLM @ Otter.ai Jan 2024 – Jan 2025 | Mountain View, California, United States ### Co-Founder and Head of ML @ Octane Security Jan 2022 – Jan 2023 | San Francisco, California, United States - Co-founded an AI company that uses ML to identify and fix vulnerabilities in smart contracts, raising $1.7M from investors like Alchemy and Symbolic Capital with advisors/angels from Meta, Apple, and Ledger - Trained a lightweight graph neural network (GNN) with NetworkX and PyTorch Geometric to classify cross-contract and cross-function reentrancy from control flow graphs (CFG) - Spearheaded an effort to use SFT and RLHF to finetune and steer LLMs (Llama2-70B and Mistral-Medium) into identifying vulnerable code that evaded all traditional static analyzers like Slither - Augmented a fuzzer with a small Transformer fitted with a custom vocabulary and tokenizer to filter transactions based on the likelihood that they broke predefined invariants created in Echidna - Established a robust hosting and infrastructure environment using AWS SageMaker, Docker, and Modal for efficient model training, testing, and deployment, streamlined with Terraform for seamless automation and scalability ### Machine Learning Intern - NeuroToolbox Laboratory @ Duke University Jan 2020 – Jan 2023 | Durham, North Carolina, United States - Collaborated with postdocs to create a shallow U-Net in TensorFlow to segment neurons in 2-photon calcium imaging videos called SUNS - Created an active learning pipeline that selectively labels neurons based on their frequency and SNR in successive SUNS runs, which reduces the need for labeled neurons to reach SOTA by 50x - Developed sets of Bash scripts for automating jobs across GPU clusters and monitored active learning jobs using custom Python scripts to probe aggregated results ### Machine Learning Engineer @ Superb AI Inc. Jan 2022 – Jan 2022 | San Mateo, California, United States - Implemented an approach in PyTorch to estimate training data influence by tracing gradient descent with Weights and Biases logs and Torch Studio - Extended approach to object detection for detection of false negatives in human-labeled datasets; treated mislabeled instances as its own class based on mixed confidences from its original labeled classes - Detected over 50% of false negatives in self-driving datasets and reported them to human labelers as a form of feedback; deployed feedback model on AWS SageMaker ### AiFi Computer Vision Team, Summer Intern @ AiFi Inc. Jan 2021 – Jan 2021 | Santa Clara, California, United States - Led a team of 3D engineers and ML research scientists in leveraging domain-randomized synthetic data to train product recognition algorithms, utilizing Unity for simulation and PyTorch for model development, hosted on Azure Cloud for scalable processing - Developed a product auto-labeling pipeline that utilizes instance segmentations from simulation data, streamlining the labeling process and enhancing model training efficiency - Created photorealistic data by using cycle-consistent GANs to transform unpaired real and simulation images, closing the domain gap improved model generalization to in-store data - Implemented a hybrid training approach using synthetic data to pretrain YOLOv5 and a small set of real examples for Supervised Fine-Tuning (SFT), significantly reducing store deployment time from 2 weeks to 2 days and boosting new SKU detection by 80% ### Research And Development Intern @ Stanford University School of Medicine Jan 2019 – Jan 2021 | Stanford, California, United States - Conducted experiments to increase the NGFR+ percentage (editing rate) for FOXP3 gene editing in hematopoietic stem progenitor cells (HSPCs) at the Bacchetta Lab - Utilized a design of experiments approach to optimize CRISPR-Cas9 editing of HSPCs, resulting in a published paper in Cytotherapy Journal - Performed cost analysis to demonstrate a reduction in the cost of reagents for gene editing experiments - Designed and executed experiments using varying amounts of CRISPR-Cas9 reagents, and analyzed flow cytometry data to assess editing efficiency and cell viability ### Machine Learning Engineer @ Duke Innovation Studio Jan 2021 – Jan 2021 | Durham, North Carolina, United States - Collaborated with KORA (Kinetic Operating Room Assistant) to develop a camera-based vision system as a foundation for machine learning solutions in the operating room, focusing on autonomous lighting and camera systems for image acquisition and touchless operating area illumination - Utilized Python scripts and BLE (Bluetooth Low Energy) technology to fetch real-time 3D coordinates of the doctor's wrists, enabling precise control of the operating area's lighting - Developed a BLE-based system to dynamically adjust the light source, ensuring optimal illumination over the designated coordinates during surgical procedures ## Education ### Computer Science and Biomedical Engineering Duke University Jan 2020 – Jan 2024 ### Saratoga High School Jan 2016 – Jan 2020 ## Contact & Social - LinkedIn: https://linkedin.com/in/devin-shah - Website: https://dshah.dev/ --- Source: https://flows.cv/devins JSON Resume: https://flows.cv/devins/resume.json Last updated: 2026-03-20