Fullstack Developer & Product Designer
Anoushka
Shah
Building intelligent systems at the intersection of AI, design, and the human brain.
I'm a Cognitive Science and Data Science student at UC Berkeley with experience across AI engineering, full stack development, product design, and UI/UX. I build end to end: from LLM pipelines, knowledge graphs, and data infrastructure on the backend to React interfaces and product flows on the front end.
I've worked on agentic AI systems, personalized retrieval and recommendation engines, deep learning research, and EEG signal processing pipelines, always owning both the technical implementation and the product thinking behind it. My research background, most recently publishing in computer vision for neuroimaging, has shaped how I work: with technical precision, comfort in ambiguity, and the ability to move carefully in domains where the answers aren't obvious yet.
My background in cognitive science informs how I approach interface and system design: I think about how design decisions shape user behavior, where friction compounds, and how the structure of a system influences the way people trust and interact with it.
I'm looking for roles within AI companies where engineering and product are closely intertwined.
AI Product — Microsoft AI
MSN COPILOT DASHBOARD UI PROTOTYPE
A high-fidelity UI prototype redesigning the MSN News integration within Microsoft Copilot, built as contract work for a Microsoft AI pitch in reimaging personalized news experiences to attract a younger user base. Reimagines the dashboard as an interactive, data-rich experience — surfacing political bias, personalized topic cards, a global news map, reading stats, and Copilot chat, all within the familiar Copilot aesthetic.
Deep Learning — Medical Imaging
BREAST ULTRASOUND LESION SEGMENTATION
Developed a Lightweight 2D UNet for automated breast ultrasound tumor segmentation model with PyTorch. Optimized for efficiency; used group-convolutions to reduce parameter counts while preserving spatial detail. Addressed ultrasound noise and class imbalance using hybrid BCE-Tversky loss; achieved 0.96 accuracy and 0.80 precision with LCC-post processing to suppress false positives
AI & ML
Software Engineering
Miscellaneous
MICROSOFT AI
AI Product Engineer (Contract)
Leading 0-to-1 development of a personalized article intelligence system for MSN — highlights salient buzzwords and dynamically links semantically related recent news. Architecting LLM parsing, vectorized knowledge graph, and personalized retrieval workflows using Azure, LangGraph, FastAPI, Neo4j, and Chroma. Designing interface, interactions, and system architecture in Figma and Adobe Creative Cloud.
GOOGLE DEEPMIND
AI UX Engineer (Contract)
Agentic AI product design and Gemini alignment-system logic for the AIUX Research Team. Designed a user trust and model autonomy alignment system using RLHF, pitched to senior management. Implemented UI prototype for an agentic calendar extension with autonomy adjustment using Google API, Gemini LLM, LangChain, React, Node.js, and PostgreSQL. Led the design of a Bayesian model for a global user trust-in-automation score and calibration algorithm evaluated against distributional variance and user reliance variables. Designed decision flows, information flows, user journey, and system architecture in Figma and Adobe Creative Cloud.
AWEAR
ML Infrastructure Intern
Wearable device startup backed by Techstars. Performed cost, latency, and failure analysis of a real-time EEG signal processing pipeline with GCP and Cloud Run. Designed and implemented a feature extraction pipeline for classification of attention states from streamed EEG data with Python, BigQuery, Dataform, Vertex AI, and GCP. Conducted statistical analysis and hypothesis testing for identifying biomarkers of attention states through power spectral density. Led A/B testing sessions of prototypes for assessing hardware product design.
UCSF RADIOLOGY & BIOMEDICAL IMAGING
Research Fellow, Center of Intelligent Imaging
Selected for CI²AI Fellowship at the Neuromodulation Imaging Lab, mentored by Dr. Melanie Morrison, PhD. Created a deep-learning neuroimaging tool for efficient visualization of 3D electrode lead trajectory in postoperative DBS patients with movement disorders. Led end-to-end development of a slicewise 2D UNet model with 3D reconstruction for binary segmentation of leads from CT scans using PyTorch, Nibabel, and FSL. Shadowed intraoperative-MRI procedures and created dataset using ITK Snap to label electrode masks for NIfTI volumes. Presented at UCSF Mission Bay's Summer Symposium; published in ISMRM.
NEUROTECH@BERKELEY
Research Engineer, Neural Signals
iEEG signal processing — implemented linear encoding and MLP decoding for understanding the neural basis of music cognition. Building on results from UC Berkeley's Knight Lab (Bellier et al., 2023) by replicating the study with a vocal-instrumental data split and identifying differential patterns in active brain regions.
NEUROTECH@BERKELEY
Marketing & Design Lead
Growth, social media management, and graphic design for the largest student community of engineers and researchers in AI and BCI development. Organized the first neurotechnology student hackathon in partnership with NVIDIA AI and OpenBCI.
LET'S
COLLABORATE.
Open to fullstack engineering and product-facing roles. I love early-stage products and teams that care about craft.