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Salary
≈ $132k – $279k per year (Estimated)
Location
In office (New York)
Employment
Full-Time

Confirmed on the employer's own hiring board on Oct 16, 2026. First seen by Alion on Oct 14, 2026.

Overview
Company
Impact
Profile match
Bernard is an AI company that fixes first-visit failures in appliance repair by answering service calls, running diagnostics and predicting the right parts against live inventory. Its system sends technicians out with a plan and is live with enterprise customers, and the company hires voice agent platform engineers, machine learning platform engineers, account executives and client success managers. The company is headquartered in New York, United States.

At Bernard, we build software that helps appliance-repair service companies schedule better. Our routing engine, decides who should take a job, on which day, and in which arrival window by reasoning across technician capabilities, coverage zones, live routes, drive time, capacity goals, warranty rules, and more.

We’re hiring a Product Engineer, Routing to own the hard edge cases that make real-world fleet scheduling difficult, and to turn those lessons into product.

What you’ll do

You’ll work on two closely related problems: making the scheduling engine smarter, and making onboarding new accounts faster and more reliable.

This is the first major rock. This is a start up and the role can expand into other areas of our product as well.

You will:

- Own the scheduling engine, including candidate-date generation, fleet constraints, route timing, scoring logic, and the scheduler-facing workspace

- Investigate why tickets fail to schedule automatically, then turn repeated failure patterns into product improvements

- Improve how the system explains or hands off blocked or held scheduling decisions, so dispatchers can act on the right cause quickly

- Own the onboarding loop for new accounts by improving historical replay, simulated customer booking scenarios, and configuration tuning workflows

- Build systems that suggest changes to rosters, zones, capabilities, and guardrails, with clear review steps before anything reaches live scheduling

- Spend time directly with customer operations teams, debugging issues in the field and using what you learn to improve the product for everyone

- Work across scheduling logic, product workflows, and operational data quality to make the engine more accurate and more trusted

Who you are

You’re a product-minded engineer who likes messy real-world systems. You enjoy debugging operational edge cases, talking to users, and turning repeated manual decisions into software.

We’d love to talk if you have:

- Strong experience with TypeScript

- Comfort working across backend systems and product surfaces

- Experience with or interest in optimization, routing, dispatch, scheduling, or decision systems

- Good instincts for evaluating system quality, especially when simulation or replay can look better than reality

- A habit of digging into evidence to understand why a system made a decision

- Interest in working directly with customers and translating operational pain into product improvements

- Good judgment when working on systems that affect real customer communications and live operations

Our stack

You do not need experience with every part of our stack, but our environment includes:

- TypeScript

- Node.js

- Fastify

- Next.js

- Python

- Neo4j

- Postgres

Nice to have

- Experience with field-service, routing, dispatch, or workforce scheduling products

- Experience building evaluation systems for AI agents or simulation-based testing tools

- Experience working with field operations, trades businesses, or logistics-heavy workflows

- Forward-deployed, implementation, or solutions engineering experience

Why this role matters

This role sits at the center of our product. You’ll shape how scheduling decisions are made, how new customers get live, and how much of that work can be automated with confidence. Your work will directly affect whether more tickets are scheduled automatically, whether dispatchers trust the system, and whether new accounts can go live in days instead of weeks.

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