Hello, I'm Sidd! I'm Siddharth Vohra, a Senior AI Research Engineer based in Pittsburgh, PA. I build AI-native products at Amazon Web Services and am pursuing a Master's in Computer Vision at Carnegie Mellon University's School of Computer Science (Robotics Institute).

Before this, I built production ML systems for AWS Bedrock, Amazon Transcribe, and Amazon Translate, working across speech and language. I earned my Bachelor's in Mathematics-Computer Science from UC San Diego in 2022.

02

Experience

Amazon Web Services

Aug 2022 — Present

  1. Senior AI Research Engineer

    Oct 2026 — Present · AWS Frontier AI Engineering and Services · Pittsburgh, PA

  2. AI Research Engineer

    Sep 2025 — Sep 2026 · AWS Frontier AI Engineering and Services · Pittsburgh, PA

    Machine Learning Engineer until Aug 2026

    Sole engineer on an AI-native engineering platform and multi-agent system that lets enterprise and public-sector teams create, deploy, govern and audit AI agents in one place. Lead agentic AI projects for AWS partners and customers, running working sessions with C-suite executives at a European insurer and a US public-sector energy organization.

  3. Software Development Engineer II

    Apr 2024 — Sep 2025 · AWS AI · Bedrock Generative AI Services · Seattle, WA

    Feature lead across Amazon Transcribe, Bedrock Data Automation and Amazon Translate. Built speech-recognition models up to 30% faster and 2x more accurate on multilingual transcription, and served as technical lead for Amazon Translate across 30+ enterprise customers.

  4. Software Development Engineer

    Aug 2022 — Apr 2024 · Lambda · App Runner · Elastic Beanstalk · Seattle, WA

    Led the AWS WAF integration into the Copilot CLI so App Runner applications ship secure by default, built an automated testing suite for the App Runner console, and helped launch App Runner in three new regions.

    Using WAF with App Runner in Copilot, AWS Copilot CLI Blog(opens in a new tab)

Teradata

Jul 2021 — Sep 2021

  1. Software Engineer Intern

    Jul 2021 — Sep 2021 · San Diego, CA

    Restructured large-object storage in the TeraCloud architecture, improving computation efficiency by roughly 50%.

03

Research & Publications

EMNLP 2026 Workshop Grounding Language Models: Learning Faithfully and Efficiently (GroundLM)

When Models Defer to Wrong Answers: A Robustness Audit of Source-Attributed Cues in Multiple-Choice QA

Manikandan Ravikiran, Siddharth Vohra

Across 220,000 responses from four models in English, Hindi, Bengali, Tamil and Telugu, an unverified expert cue pulled models to a named wrong option in 41.1% of trials they had first answered correctly. A majority cue did so in 12.5%.

Accepted, to be presented · 29 October 2026 · Budapest, Hungary

MICCAI 2026 Workshop Agentic AI for Medicine (MedAgent)

Absent-Byte Diagnoses: Auditing Structured Medical VLM Interfaces

Siddharth Vohra, Manikandan Ravikiran

Three of five medical vision-language models returned a structured diagnosis in 617 of 3,000 calls where the prompt claimed an image was attached but the request carried no image bytes. A client-side verifier blocked all 5,047 tested evidence-binding violations.

Published, open access · 1 October 2026 · Strasbourg, France

ACM ICMR 2026 Workshop Toward Trustworthy Vision-Language Models in the Wild (TrustVLM) Featured In Press

Hearsay: Vision-Language Medical Diagnoses Without an Image

Siddharth Vohra

Audits frontier vision-language models under missing-image medical prompts and identifies structured diagnostic confabulation, demographic sensitivity, and structured-output failure modes.

Presented, non-archival · June 2026 · Amsterdam, The Netherlands

COLING 2025 Main conference

TEEMIL: Towards Educational MCQ Difficulty Estimation in Indic Languages

Manikandan Ravikiran, Siddharth Vohra, Rajat Verma, Rohit Saluja, Arnav Bhavsar

Introduces the TEEMIL-H and TEEMIL-K datasets for Hindi and Kannada MCQ difficulty estimation, with multilingual model baselines and ablations over context, answer options and none-of-the-above options.

January 2025 · Abu Dhabi, United Arab Emirates

COMSYS 2021 Springer Lecture Notes in Networks and Systems, vol. 404

You Reap What You Sow—Revisiting Intra-class Variations and Seed Selection in Temporal Ensembling for Image Classification

Manikandan Ravikiran, Siddharth Vohra, Yuichi Nonaka, Sharath Kumar, Shibashish Sen, Nestor Mariyasagayam, Kingshuk Banerjee

Studies how intra-class variability, seed size and seed selection affect semi-supervised Temporal Ensembling performance across image-classification datasets. Ravikiran and Vohra contributed equally; names are ordered alphabetically.

2023

arXiv 2020 Preprint

Investigating the Effect of Intraclass Variability in Temporal Ensembling

Siddharth Vohra, Manikandan Ravikiran

Early study of how within-class variation and the number and choice of labelled seeds affect Temporal Ensembling. Led to the 2023 Springer paper above.

20 August 2020

04

Academic Service

Conference peer review

Reviewer and programme committee member for workshops and affinity events at NeurIPS, ICML, EMNLP, ECCV, MICCAI, COLM, IJCAI-ECAI and ACM KDD, and ethics reviewer for the NeurIPS 2026 main conference, covering mechanistic interpretability, biomedical retrieval, and the robustness and safety of agentic AI systems.

NeurIPS 2026
Ethics reviewer, main conference · Workshops: Symmetry and Geometry in Neural Representations · GlobalSouthAI (affinity workshop) · Trustworthy AI for Good · AI for Meta-Science: Scaling and Organizing Science in the Age of AI Scientists · AI for Drug Discovery: Bridging the Translation Gap · Interpretability for Discovery: Understanding and Discovering Novel Knowledge in AI Models · Mathematical Reasoning and AI · Grounded and Faithful Vision-Language Models for Real-World Deployment · Continual Learning for Enterprise AI Agents · Dynamics at the Frontiers of Optimization, Sampling, and Games · AI and the Self: Human Identity, Authenticity and Agency in the Age of AI
EMNLP 2026
Workshops: Grounding Language Models: Learning Faithfully and Efficiently · Workshop for Research on Agent Language Models
COLM 2026
Workshops: Context Beyond the Window: Persistent Knowledge in Language Models · Social Simulation with LLMs: Fidelity in Applications · Workshop on Efficient Reasoning
ECCV 2026
Workshops: Women in Computer Vision · Computer Vision for Ecology
ICML 2026
Workshop: Compositional Learning: Safety, Interpretability and Agents
MICCAI 2026
Workshop on AI for Safe Surgery
ACM KDD 2026
Programme Committee · Workshop: Agentic Software Engineering (SE 3.0): The Rise of AI Teammates
IJCAI-ECAI 2026
Diversity and inclusion activity: Global South AI

Hackathon judging

Invited to the judging panels of flagship university hackathons at Carnegie Mellon, UC San Diego and Cincinnati. HackCMU and DiamondHacks each had about 100 submitted projects.

HackCMU 2025
ACM@CMU, Carnegie Mellon University · September 2025 · 600+ participants, 100+ submissions
DiamondHacks 2025
ACM at UC San Diego · April 2025 · 650+ registrations, 95 projects
RevolutionUC 2025
ACM@UC, University of Cincinnati · March 2025 · 873 applications, 300 selected
Hackathon Raptors
Elected member, Guild of Expert Engineers · August 2026 · Elected by peer review. Election requires at least four of five votes from randomly selected sitting Fellows against eight published criteria.
05

Honors & Memberships

Google

2026

Gemini Academic Program Award

$20,000 in Google Cloud credits for Gemini foundation-model research and multimodal evaluation audits.

IEEE

Membership for 2026

Member, Pittsburgh Section

Member in good standing of the Institute of Electrical and Electronics Engineers, valid through December 2026.

ACM

Member since May 2026

Professional Member

Admitted to the Association for Computing Machinery having fulfilled the requirements for Professional Membership.

06

Education

Carnegie Mellon University

Expected Dec 2026

M.S. Computer Vision

School of Computer Science, Robotics Institute.

University of California, San Diego

2019 — 2022

B.S. Mathematics-Computer Science

Cum laude, Provost Honors all quarters. Founding and principal member of the Machine Learning Club.