Manasi Sharma
Hi there! I'm a Research Engineer at Scale AI, where I work on evaluations, benchmarking, and reinforcement learning / post-training research, to train frontier models with better reasoning capabilities for state-of-the-art agents like browser & computer-use agents. My work has been published at conferences such as ICLR, ICML, NeurIPS, and CoRL. Previously, I was a Research Scientist at MIT Lincoln Laboratory. I earned my MS in Computer Science (AI track) from Stanford and my BA in Computer Science and Physics from Columbia. I'm a generalist passionate about applying AI to diverse real-world challenges, from astronomy to autonomous vehicles to agents.
I am fortunate to have worked with many different professors over the course of my academic career. I conducted research in the ILIAD Lab under Prof. Dorsa Sadigh at Stanford on robotic learning using language-conditioned diffusion models, and the Stanford Vision Lab under Prof. Fei-Fei Li and Prof. Jiajun Wu on the BEHAVIOR robotic simulation benchmark. As an undergrad, I worked with Prof. Daniel Hsu and Prof. Zoltan Haiman on interpretable deep learning for astrophysics, and at Caltech with Prof. Mansi Kasliwal on image classification for the Gattini-IR telescope.
My research interests include AI evaluation methodologies, agents, reinforcement learning, and AI safety and alignment.
Everything else: Outside of work, I love dancing (I was Captain of the Columbia Raas dance team), playing the keyboard and exploring the natural landscapes in the Bay Area!
Updates
- Apr 2026 Rubric Robustness and SpreadsheetArena accepted at ICML 2026
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Jan 2026
ResearchRubrics, a benchmark to evaluate deep research agents, was accepted at ICLR 2026
It was also referenced in a Perplexity AI deep research release and in the RLHF Book - Jan 2026 I recorded a podcast! Check out the Chain of Thought podcast episode on ResearchRubrics
- Jan 2026 MCP-Atlas preprint released and was referenced in OpenAI & Anthropic model card releases
- Oct 2025 Remote Labor Index preprint released, in collaboration with the Center for AI Safety at UC Berkeley
- Oct 2025 Adaptive Guidance accepted at the NeurIPS 1st Efficient Reasoning Workshop
- Mar 2025 Joined Scale AI as ML Research Engineer on the Reasoning & Agents Research Team!
- Jun 2024 Why Would You Suggest That? presented at ICML Workshop
- Dec 2023 Exploring the Spatial Reasoning Abilities of LLMs presented at NeurIPS Instruction Tuning Workshop
- Sep 2023 My Python tutorial for Stanford's CS 224N course was released on YouTube
- Sep 2022 BEHAVIOR-1K accepted at CoRL 2022 and nominated for Best Paper
Education
Stanford University, School of Engineering
Master's in Computer Science
Advisors: Dorsa Sadigh (ILIAD Lab), Fei-Fei Li, Jiajun Wu (Stanford Vision Lab)
Columbia University, Columbia College
BA in Computer Science with concentration in Physics
Advisors: Daniel Hsu, Zoltan Haiman
Work Experience
Scale AI
Mar 2025 — Present- Research Lead for computer-use & browser-use agents, spearheading 3 first-author publications (7 total) on evaluations at top-tier conferences (ICLR, ICML '26); including ResearchRubrics, referenced in a Perplexity AI agent release.
- Collaborated with external organizations like the Center for AI Safety at Berkeley on benchmarks for economically useful tasks (Remote Labor Index) and tool-use (MCP-Atlas), referenced in OpenAI & Anthropic's latest model cards.
- Post-trained models with RL to analyze strategies for optimal data learning and contributed to research on an efficient RL algorithm with intermediate rewards (NeurIPS '25 workshop).
- Collaborated with researchers at top frontier labs, offering consultation on advanced computer-use data types involving programmatic & agentic verifiers.
MIT Lincoln Laboratory
Aug 2023 — Mar 2025- Spearheaded a project on LLM explainability & trust that was published at an ICML '24 workshop, & built an open-source LLM-testing framework (maite). Participated in a project collaboration with MIT Profs. Philip Isola & Jacob Andreas.
- Architected an internal tool for efficient evaluation of a Retrieval Augmented Generation (RAG) system with 15+ distinct metrics & assessed it on referencing past recordings in a speech-to-text application, achieving 92% accuracy.
Nissan Research
Jun 2022 — Sep 2022- Engineered an end-to-end LiDAR 3D point-cloud classification system for autonomous vehicles, achieving >95% accuracy, ~2% FPR and 85% reduction in runtime. Deployed in Nissan Autonomous Vehicles beginning Winter '22.
- Ideated research projects on language models & diffusion models and attended various mentorship events as part of Google's CSRMP.
Publications
Teaching Experience
Stanford School of Engineering
Graduate Teaching Assistant
- Managed weekly Discussion Sections of 75+ students for some of the most popular CS courses at Stanford (>600 students); held office hours, constructed & graded HWs. Received >95% excellent reviews.
- Courses: CS 224N (NLP, Prof. Manning), CS 231N (Computer Vision, Prof. Fei-Fei Li), CS 230 (Deep Learning, Prof. Andrew Ng)
- Recorded the Python tutorial for Stanford's CS 224N course on YouTube
Columbia University, Department of Mathematics
Undergraduate Teaching Assistant for Calculus III (across 4 semesters)
- Graded assignments, led weekly office hours for ~150 students. Consistently received >80% excellent reviews.
Community Engagement
Graduate Community Chair, Stanford Women in Computer Science
- Organized multiple events for the graduate community including mixers, industry alumni panels, lunches, talks, etc.; spearheaded a focus on MS students through the setup of an MS student alumni panel and care-package mixer.
- Managed over $3000 in funds from the CS department and Engineering schools to fund events for graduate students.
Honors
- 1 of 25 students accepted to the highly selective Pear Garage program for Entrepreneurship at Pear VC — Oct '22
- 1 of 18 accepted to the highly selective GFSD (Graduate Fellowships for STEM Diversity) Program — Mar '22
- 1 of 50 accepted into Google's CS Research Mentorship Program (CSRMP), Class of 2022A — Feb '22
- Selected for the final round of the GEM Fellowship — Jan '22
- Dean's List (in 6 out of 7 graded semesters, awarded to top 20%), Columbia University — Fall '17 - Fall '20
- Columbia Undergraduate Research Fellowship (URF), Columbia College Summer Funding Program — May '20
- Visiting Undergraduate Research Program (VURP) Award, California Institute of Technology — May '19
- 1 of 25 awarded Laidlaw Undergraduate Research & Leadership Scholarship, Columbia University — '18 – '19
- Andy Grove Scholarship for Intel Employees' Children, Intel Foundation — Fall '19
Areas of Experience
Deep Learning, Natural Language Processing, Generative AI, Agents, Reinforcement Learning & Decision Making, Computer Vision, Diffusion Models, Explainability, Robotics, & Graph Neural Networks.