Founding Engineer

Overview

Self-driving companies record every mile a car drives. Nobody records how it gets fixed. Repair expertise lives in technicians' heads: undocumented, unstructured, and disappearing as their shortage grows.

Bayworks captures voice and video data as technicians repair cars and verifies the work. Every repair becomes ground truth for how cars get fixed: symptom, diagnosis, fix, outcome. This compounds into a system that runs the dealership's operations and standardizes every repair, making it faster and compliant with manufacturer specifications.

Why now

In 110 days, we've gone from 0 to 80+ dealerships covering 4,000 cars a day, including a top-50 US dealer group ($3B+ annual revenue) and have hit $XMn ARR. We know what's working.

Now we need to get to 1000 rooftops / $10M ARR. To get there we need someone to own our data: turning every repair we capture into ground truth, and building the models that run on it. The data is there; you turn it into the product.

World-class team

We're engineers from BITS Pilani.

Kaustubh (Founder & CEO) built voice AI at Sarvam (India's frontier AI lab) deployed across government and public enterprises, and before that worked in automotive retail lending with carmakers. Shubham (Founder & CTO) built the underwriting engine at Navi, a $2B fintech, powering $10M+ in loans every day, and the mobile app at Scapia, which scaled to millions of users.

Investors

Backed by Y Combinator (F26)

What you'll work on

Turn raw voice, video and dealership data from every repair into structured ground truth: symptom, diagnosis, fix, outcome. This is a hands-on role; what you build ships to production Build the evals that tell us whether our AI got a repair right, and own the metrics we ship against Run experiments across speech and language models and decide what goes to production Build models on repair data that standardize the next repair: the likely diagnosis for a symptom, and what a fix needs to meet manufacturer specifications Spend time in service bays with technicians and feed back what you learn into the product

You can be a great fit if

You've shipped ML or data products to production, not just notebooks You're comfortable with messy real-world data: noisy shop-floor audio, free-text repair notes, inconsistent dealership records You're strong in Python and SQL, and have built evals for LLMs, speech or multimodal models You move fast and don't overthink You care whether the model works in the service bay, not just on the benchmark You want to be at a company that's early enough that your work actually matters

Compensation

This role includes: 160K - 200K base salary Meaningful equity Medical, dental, and vision insurance Free DoorDash vouchers for everyday meals

About the interview

1. 30 mins with the founders 2. Assignment: a short take-home on a sample of real (anonymized) repair data 3. Offer