About the Forward Deployed Engineer Role at Hatch

The Forward Deployed Engineer (FDE) at Hatch is a hands-on, code-level role embedded alongside Hatch's most strategic clients, with a primary focus on the company's Voice AI product. You ship production-quality code into Hatch's core codebase, internal tooling, and the integration layer that connects Hatch's products to client systems. "Forward deployed" describes where you sit: close to customers, diagnosing implementation problems firsthand, then deciding whether a fix belongs in middleware or in Hatch's core code, and shipping it either way. You will own technically complex onboardings end to end (CRMs with no existing connector, multi-system orchestrations, custom business logic, event-based triggers) while hunting for the reusable pattern underneath.

This is a role for a builder who thrives on autonomy and holds a high correctness bar. Hatch expects you to push PRs without heavy oversight, QA and monitor what you build, and partner directly with Product to turn implementation learnings into first-class product features. The posting targets 1 to 4 years of software engineering experience with hands-on Voice AI exposure, comfort in Python and/or Elixir (Go in scope), and experience using and extending automation tooling like n8n. Strong communication with both business and technical audiences is essential, since you also own the client's technical relationship.

Quick Stats

* Typical process: 4 to 5 rounds, roughly 3 to 5 weeks end to end

* Format: Recruiter phone screen, then remote video technical and behavioral rounds (this role can be fully remote in the US)

* Core focus: Voice AI systems, Python/Elixir/Go, API and webhook integrations, automation tooling (n8n), client-facing communication, production-grade autonomy

* Difficulty: Moderate to hard, because the role blends real coding depth with client-facing judgment and demands you decide where a fix belongs (core vs. middleware)

What Hatch Looks For

* 1 to 4 years of software engineering with hands-on Voice AI experience; consulting or client-facing work is a plus

* Fluency in Python and/or Elixir (Go in scope) plus experience building on automation tooling like n8n

* Judgment to tell a core-code change from a middleware or automation problem across a broad tech ecosystem

* Strong communication with technical and business audiences, plus a high correctness bar under real autonomy

Round 1: Recruiter Screen (~30 minutes)

What to Expect

A recruiter or talent partner opens with a conversational call to confirm you meet the core bar, gauge your motivation, and walk through logistics. Expect questions about your software engineering background, any Voice AI or client-facing experience, US location and remote-work eligibility, and salary expectations against the posted $144,000 to $200,000 range. This is also where you deliver a crisp "why Hatch" and show you understand that the FDE role is code-level and customer-embedded, not pure project management.

Example Questions

* "Walk me through your background and what drew you to a forward deployed engineering role."

* "What hands-on experience do you have with Voice AI systems or the modern voice stack?"

* "Are you located in the US and comfortable with a fully remote setup that may include occasional onsite client visits?"

* "What are your salary expectations for this role?"

Tips

* Lead with concrete Voice AI or integration work; name the languages (Python, Elixir, Go) and tooling you have shipped with.

* Show you understand the "forward deployed" model: embedded with clients, shipping to core code, owning the technical relationship.

* Use Nora's Standard Mode to rehearse a tight two-minute background pitch and a clear "why Hatch" so the classic phone-screen mix feels natural.

Round 2: Technical Coding and Integration Round (~60 minutes)

What to Expect

A live coding and problem-solving session with an engineer, focused on the practical work of the role rather than abstract algorithm puzzles. Expect to write or read code in Python or Elixir, reason about building integrations and connectors (direct API integrations, webhook endpoints, event-based triggers), and handle realistic edge cases. Because Hatch holds implementations to a "right, not just working" standard, expect follow-ups on correctness, error handling, testing, and monitoring.

Example Questions

* "Design and sketch code for a webhook endpoint that ingests booking events from a client CRM and triggers a downstream action in our platform."

* "Here is a snippet of Elixir (or Python). Walk me through what it does, then extend it to handle a failure case."

* "How would you build an integration to a CRM that has no existing Hatch connector, and how would you QA it before go-live?"

* "How do you decide whether a client problem should be solved in middleware or automation tooling versus a change to core code?"

Tips

* Talk through your reasoning out loud; the interviewer wants to see how you scope, handle ambiguity, and pick where a fix belongs.

* Cover the full lifecycle: build, QA, test before go-live, then monitoring and iteration afterward.

* Practice this with Nora's Technical Mode so you can narrate integration design, edge cases, and testing under time pressure.

Round 3: System Design and Voice AI Deep-Dive (~60 minutes)

What to Expect

A design-oriented conversation centered on the modern voice AI stack and complex, multi-system onboardings. You may be asked to architect a client implementation involving real-time voice agents, telephony, text-to-speech and voice quality, LLM orchestration, and CRM or booking integrations. The interviewer is probing whether you can navigate a broad technology ecosystem, read comprehensive developer documentation, and spot the reusable pattern that could graduate into a first-class product feature owned by an R&D team.

Example Questions

* "Design an end-to-end voice agent flow that books appointments and syncs them to a client's CRM. Where do latency and voice quality matter most?"

* "A recurring implementation pattern keeps showing up across strategic clients. How do you decide it deserves dedicated R&D investment, and how would you build the first-class version?"

* "Walk me through orchestrating multiple client systems with custom business logic and event-based triggers."

* "How would you monitor a live voice deployment so you know something degraded before the customer does?"

Tips

* Anchor answers in the voice stack named in the posting: real-time agents, telephony, TTS, LLM orchestration, CRM/booking.

* Always surface the reusable pattern and where it belongs (core product vs. per-client middleware), since that judgment is central to the role.

* Rehearse structured design walkthroughs with Nora's Technical Mode to keep your architecture explanations organized and specific.

Round 4: Behavioral and Client-Facing Round (~45 minutes)

What to Expect

A conversation with the hiring manager and possibly Product about how you operate: shipping fast with autonomy, owning correctness, communicating with clients, and partnering across teams. Since the FDE owns both the technical work and the client's technical relationship, expect STAR-style questions on client consulting, resolving ambiguous requirements before building, giving proactive status updates, and knowing when to escalate. Onsite client visits and presenting architectural solutions may come up.

Example Questions

* "Tell me about a time you owned a complex project end to end with little oversight. How did you ensure it was correct, not just functional?"

* "Describe a situation where client requirements were ambiguous. How did you resolve them before you started building?"

* "Give an example of translating something you learned from a customer into a product or process improvement."

* "How do you communicate technical status and blockers to a non-technical business stakeholder?"

Tips

* Prepare STAR stories that show autonomy, high correctness bar, and clear communication with both technical and business audiences.

* Emphasize that you own the technical relationship, not project management, and show how you keep clients informed proactively.

* Use Nora's Behavioral Mode to practice STAR answers on ownership, ambiguity, and client communication until they are concise and specific.

Frequently Asked Questions (FAQ)

1) How many rounds are there?

Hatch has not published a formal process for this role, so this is a realistic expectation rather than a confirmed sequence: plan for about 4 to 5 rounds, starting with a recruiter screen and followed by a technical coding/integration round, a system design and Voice AI deep-dive, and a behavioral plus client-facing round. Some stages may be combined depending on scheduling.

2) What topics are most common?

* Voice AI systems and the modern voice stack (real-time agents, telephony, TTS, LLM orchestration, CRM/booking integrations)

* Building integrations and connectors (APIs, webhooks, event-based triggers), automation tooling like n8n, and coding in Python, Elixir, or Go, plus client-facing communication and ownership

3) How long does the process take?

Expect roughly 3 to 5 weeks from recruiter screen to offer, depending on scheduling and how quickly interview loops are coordinated. Fully remote logistics can speed things up, while coordinating multiple engineer and Product interviewers can add time.

4) How should I prepare?

* Study the posting's stack closely: brush up on Python and/or Elixir (and Go), integration patterns, and the modern voice AI components named in the role.

* Prepare STAR stories that highlight end-to-end ownership, resolving ambiguous requirements, correctness under autonomy, and communicating with business and technical audiences.

* Be ready to reason about where a fix belongs (core code vs. middleware vs. automation tooling) and how you QA and monitor implementations before and after go-live.

* Run mock interviews with Nora: use Standard Mode for the recruiter screen, Technical Mode for the coding and system design rounds, Behavioral Mode for the client-facing round, and Salary Negotiation Mode to practice the offer conversation against the $144,000 to $200,000 range.