Lead Product Engineer, AI

Bernardhq; New York (NY); On-site; USD 140,000 - 190,000; Full time

Job summary

Bernard in New York City seeks a software engineer to own production systems at the intersection of AI and traditional infrastructure. You will ship code that changes diagnostics, dispatch workflows, and parts forecasting, directly impacting field operations.

You will work closely with the CTPO and the founding engineering team, embracing autonomy, speed, and a hands-on approach to delivering value to customers and technicians in the field.

Qualifications

• Shipped LLMs or agents to production
• Strong in TypeScript and Python
• Autonomy and ability to work at startup pace
• Experience with production-grade systems and real-time services

Responsibilities

• Own production systems serving real customers, ensuring reliability and performance
• Ship code that improves diagnostics, routing, and operational workflows
• Collaborate directly with CTPO and founding engineering team, with minimal PM mediation
• Build APIs and services powering the Bernard operating system

Skills

TypeScript

Python

LLMs in production

Tools

Python

TypeScript

Job description

About Bernard

Half of all appliance repair visits fail on the first trip. Wrong part. Wrong diagnosis. The homeowner waits another week. The company eats another truck roll. Multiply that across millions of service calls a year and you're looking at a broken layer of American infrastructure that nobody's talking about.

Bernard fixes this at the point of first contact. Our AI answers the call, runs the diagnostic, predicts the right parts against live inventory, and sends the technician out with a game plan. We don't just book the job. We solve it before the truck rolls. The result: first-visit fix rates go from 50% to 80%.

We're live with enterprise customers, growing fast, and building the operating system for field service from our office in NYC. Every hire here touches the product, the customer, and the trajectory of the company.

The Role

You own production systems that serve real customers today. This is software engineering at the intersection of classical systems and cutting-edge AI - multimodal agents, real-time voice infrastructure, intelligent retrieval, and mobile interfaces that field technicians actually use.

You will ship code that changes how a technician diagnoses a broken compressor, how a dispatcher in Chicago routes a truck, and how a parts distributor in Dallas predicts demand. The feedback loop between your code and real-world outcomes is measured in days, not quarters.

You work directly with the CTPO and the founding engineering team. No product managers translating requirements. No sprint ceremonies slowing you down. You talk to customers, you see the problem, you build the fix.

What You'll Build

• The platform that appliance repair companies use to manage calls, diagnostics, work orders, and technician dispatch

• Real-time speech-to-speech conversational agents for voice and video with field technicians

• AI agents that join customer conversations across channels and act on messages

• Production APIs and services that power the entire Bernard operating system

• Intelligent retrieval systems using graph databases, vector search, and MCP for appliance diagnostics

• Internal tools and dashboards that give our team and customers visibility into everything happening across the platform

Stack

Python and TypeScript. The rest is modern, production-grade, and changes fast. If you're good, you'll pick it up.

You Should Apply If

• You've shipped LLMs or agents to production, not just prototypes
• You're strong in TypeScript and Python
• You want real problems, not research projects
• You thrive with autonomy - we don't micromanage because we hire people who don't need it
• You're energized by speed, not stressed by it
• You can get excited about helping a technician in Tulsa fix a dishwasher faster

How We Work

We are a small team. Everyone owns their domain end to end. There is no middle management, no committees, and no approval chains. If something needs to happen, you make it happen. We work in person in New York City. We move fast, give direct feedback, and hold each other to a high bar. The pace is startup pace — if that energizes you, you'll love it here.