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