# Principal Physicist & Curriculum Developer at Meraki Labs

> Onsite · India · Principal · Software engineering · open. Source: https://alderhq.app/jobs/ashby%3AMeraki-Labs%3Ab5dfd276-7d3b-4669-b1f6-00a254f22b21 (read 2026-11-08).

## Facts (observed)

- Company: [Meraki Labs](https://alderhq.app/companies/meraki-labs)
- Location text: Bangalore, India
- Where it can be done from (inferred): Onsite · India (evidence: "Location lists "bangalore"")
- Posted: 2026-04-16 (206 days ago)
- First seen by alder: 2026-11-07 · last verified: 2026-11-07
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://jobs.ashbyhq.com/Meraki-Labs/b5dfd276-7d3b-4669-b1f6-00a254f22b21
- Skills listed (inferred): LLMs

## Pay and sponsorship

- Posted pay: not posted
- Market band for similar posted roles (Bengaluru, n 3): p25 ₹84.3L, median ₹98.5L, p75 ₹98.5L

## Description (the employer's text)

### About Meraki Labs

Meraki Labs is at the forefront of India's deep-tech innovation landscape, operating as a dynamic venture studio founded by visionary entrepreneur Mukesh Bansal (Founder - Myntra, Cure Fit) and Peeyush Ranjan (Former Google VP and Flipkart CTO). Our mission is to build and rapidly scale AI-first, "moonshot" startups—transforming groundbreaking ideas into next-generation industry leaders. Through a hands-on, intensive partnership model, we work closely with exceptional founders, driving these ventures from concept to impact.

### Role Overview

We are seeking a seasoned Professor/Academician to serve as the Principal Subject Matter (Physics and Mathematics) for our AI Tutor. Your mission is to ensure that the AI Tutor doesn’t just provide answers, but facilitates true learning. You will act as the ultimate "Sensei" for our AI models, evaluating the tutor’s reasoning, the rigor of our Learning Management System (LMS) content, and the pedagogical effectiveness of our step-by-step guidance.

### Key Roles & Responsibilities

### 1. AI Tutor Audit

Logic Validation: Review AI-generated hints to ensure they align with teaching methods and guiding students to the answer without "spoiling" the breakthrough moment.

Accuracy Oversight: Conduct deep-dive audits into complex topics (Quantum Physics/Math) to identify "hallucinations" or mathematically sound but pedagogically confusing steps.

Tone & Empathy Monitoring: Evaluate the AI’s "non-judgmental" persona. Ensure the feedback encourages a growth mindset and manages a student’s "productive struggle" effectively.

### 2. Curriculum & LMS Strategy

Content Mapping: Oversee the mapping of our Curriculum to ensure the difficulty progression is academically sound.

LMS Integrity: Review the structure of lessons within the LMS to ensure logical flow, concept scaffolding, and comprehensive coverage of learning objectives.

Knowledge Graph Refinement: Work with the engineering team to refine the "Concept Graph," ensuring the AI understands which foundational concepts a student is missing based on their mistakes.

### 3. Product Intervention & Feedback Loops

SME Input for Engineering: Translate complex teaching methodologies into actionable insights for AI prompt engineers. (e.g., "In this Calculus problem, the AI should prompt for the Chain Rule here instead of simplifying the constant.")

Gap Analysis: Identify areas where the AI fails to understand non-standard student approaches or creative problem-solving techniques.

Efficacy Research: Help design and monitor "learning gain" metrics and measure if students are actually learning

### Ideal Candidate Profile

Academic Background: PhD, Assistant Professor, or Senior Professorship in Physics, Engineering, or Mathematics with 5+ years of teaching experience.

Tech-Forward: While you don't need to code, you must be comfortable using AI tools and have an intuitive grasp of how LLMs interact with technical content.

The Recruiter for this role is Ritesh Kalvellu - ritesh@merakilabs.com. You can connect with him for queries about the role.

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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/ashby%3AMeraki-Labs%3Ab5dfd276-7d3b-4669-b1f6-00a254f22b21 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.