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Forward Deployed Software Engineer; AI Agents

Job in New York, New York County, New York, 10261, USA
Listing for: Pallet
Full Time position
Listed on 2026-03-02
Job specializations:
  • Software Development
    AI Engineer, Software Engineer, Machine Learning/ ML Engineer
Salary/Wage Range or Industry Benchmark: 80000 - 100000 USD Yearly USD 80000.00 100000.00 YEAR
Job Description & How to Apply Below
Position: Forward Deployed Software Engineer (AI Agents)
Location: New York

Forward Deployed Software Engineer (AI Agents)

San Francisco or New York City

About Pallet

Pallet is building AI Agents to transform the $12 trillion logistics industry, backed by $50M from investors including General Catalyst, Bessemer Venture Partners, and Bain Capital Ventures. Our mission is to automate the physical economy by creating the most reliable intelligence infrastructure for logistics.

Our agents excel at handling complex logistics workflows—scheduling, data entry, and load management—for Fortune 500 companies. But this is only the beginning. Our vision is to be the go‑to answer for AI in logistics: from proprietary models to a suite of tools that turn messy business rules into production‑ready context for LLMs.

We're positioned to build the next $10B company in logistics. Join us and work alongside leaders from Google, Door Dash, YC, and more to shape the future of logistics tech.

About the Opportunity

We’re hiring one of our first Forward Deployed Software Engineers to embed directly with customers and deploy production AI agents inside some of the world’s largest logistics companies. You will sit alongside customer engineers and operations teams, reverse engineer legacy systems, design pragmatic integrations, and ship AI workflows into messy, real‑world environments.

This is not a research or pure backend role. This is a hands‑on deployment role focused on getting systems live, keeping them reliable, and owning real‑world outcomes.

How You Will Make an Impact
  • Reverse engineer undocumented APIs, ERPs, TMS systems, and internal tools with minimal guidance.
  • Debug broken data pipelines, mismatched schemas, and flaky integrations in production environments.
  • Sit onsite with customer teams (~25% travel) to diagnose failures and ship fixes in real time.
  • Make pragmatic tradeoffs to get systems live under tight timelines.
  • Own customer go‑lives end‑to‑end — from scoping to production stability.
Preferred Experience
  • 1–5 years of experience shipping and owning production systems in high‑growth or early‑stage environments
  • Strong experience integrating with third‑party APIs, authentication systems, and event‑driven workflows
  • Experience debugging distributed systems in production under real customer constraints
  • Comfortable working in ambiguous environments where documentation is incomplete, and requirements evolve during execution
  • Experience working directly with customers or external stakeholders is a strong plus
This role is for you if:
  • You enjoy debugging messy real‑world systems more than building pristine internal abstractions
  • You’re comfortable sitting with customers and figuring things out in ambiguous environments
  • You prefer shipping impact quickly over polishing perfect architectures
This role is likely not for you if:
  • You’re primarily interested in model research or pure ML experimentation
  • You prefer deeply scoped platform work without external customer interaction
  • You’re looking for a remote‑first environment
  • Backend:
    Node + Type Script on Encore.dev. Event‑driven design with message passing and queues. Hosted on GCP
  • LLMs:
    OpenAI, Anthropic, Google for foundation models; fine‑tuned models on Vertex AI
  • Frontend:
    Next.js + React + Type Script. Hosted on Vercel
  • Observability:
    Datadog for logging and metrics
How We Work &

What We Offer
  • Impact from Day One:
    Your first week might involve wiring a new integration into production or debugging a workflow that saves a customer hours. This isn’t demo work—it's real implementations
  • Ownership & Autonomy:
    Propose ideas, whiteboard them, ship without layers of approvals. One standup at 10 am PST—primary focus is building, not meetings
  • Close‑Knit Pods:
    We sit side by side, so questions get answered in minutes. Small PRs, fast iteration, observability‑first
  • Culture:
    Catered lunch daily at 12 pm. Thursday boba runs. AI tools encouraged (Claude, Cursor)—but you must understand the architecture
  • Compensation:
    Salary and equity at 80th percentile, plus bonuses tied to customer go‑lives. We promote from within—several teammates earned promotions in under a year
Interview Process
  • Intro Chat with Recruiting - (30 mins)
  • Technical Screen + Meet and Greet with an Engineer (60 mins)
  • Executive…
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