Forward Deployed Engineer

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, DoorDash, YC, and more to shape the future of logistics tech.

We’re hiring one of our first Forward Deployed Engineers (FDEs) to build and deploy production AI systems inside some of the world’s largest logistics companies. You’ll work directly with customers and internal teams to take systems from scoping to live production, owning integrations, reliability, and real-world outcomes. After launch, you’ll remain hands-on in production—debugging failures, improving model performance, and extending functionality based on real production data.

This is a foundational, customer-facing role at Pallet, sitting at the intersection of product, engineering, and operations. It’s an excellent fit for builders and future founders who want hands-on ownership, first-principles problem solving, and deep exposure to real customer constraints that shape products and businesses.ems, first-principles execution, and end-to-end ownership under real-world constraints.

• Operate as a true Forward Deployed Engineer. Reverse-engineer APIs, UIs, and data models with little documentation; design pragmatic integrations; and ship production systems under real-world constraints.
• Own customer outcomes end-to-end. Act as the technical point of contact, handle escalations, diagnose failures across AI behavior and integrations, and coordinate fixes with internal engineering teams.
• Scope, prioritize, and deliver under pressure. Break down ambiguous problems, estimate timelines, make smart tradeoffs, and focus on business impact over technical perfection.
• Balance speed with rigor. Maintain strong attention to detail while knowing when “speed” is the right call to keep customers moving without sacrificing “quality”.
• Build from the field. Feed learnings from deployments directly into product, platform, and tooling improvements.

• 1-5 years of experience building and operating end-to-end production systems, ideally in high-growth SaaS or AI-driven environments
• Strong technical fundamentals across APIs, authentication, data modeling, system integration patterns, and debugging complex systems
• A builder’s mindset with high ownership—you’re comfortable scoping ambiguous problems, moving quickly, and shipping durable solutions
• Experience working directly with customers or external stakeholders, translating real-world constraints into reliable technical systems
• Clear and effective communication skills across both technical and operational audiences—from frontline operators to engineering leadership
• Thrives in fast-moving, ambiguous environments, where requirements are incomplete, and problems are discovered through execution

• Backend: Node + TypeScript on Encore.dev. Event-driven design with message passing and queues. Hosted on GCP
• Database: PostgreSQL
• LLMs: OpenAI, Anthropic, Google for foundation models; fine-tuned models on Vertex AI
• Browser Automation: Playwright
• Frontend: Next.js + React + TypeScript. Hosted on Vercel
• Observability: Datadog for logging and metrics

• 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 10am 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 12pm. 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

1. Intro Chat with Recruiting - (30 mins)
2. Technical Screen + Meet and Greet with an Engineer (60 mins)
3. Executive Chat with Bizops Lead or CTO (30-45 mins)
4. Final Interview: deep-dive technical and behavioral interviews (~4 hours)

San Francisco: Since we're a small team building 0→1 products, we prefer candidates comfortable working in an in-office environment in San Francisco. We typically come into the office 5 days a week. Relocation assistance can be provided for interested candidates.

New York: Satellite office with Pallet employees across engineering, operations, and success—we're seeking to hire and grow out the team here. The team typically comes into the office 5 days a week. Relocation assistance is not provided for our New York office.

Travel: ~25% travel to customer locations

The estimated salary range for this role is $160,000–$240,000, depending on experience and skill set. In addition to base salary, we offer competitive equity, benefits, and growth opportunities. Final compensation will be determined based on a combination of factors, including experience, qualifications, and location.

Pallet is proud to be an Equal Employment Opportunity and Affirmative Action employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.