Forward Deployed Product Engineer
Pallet
| Company | Pallet |
| Category | Engineering |
| Location | San Francisco or New York |
| Remote | On-site (inferred) |
| Employment | Not stated |
| Level | Not stated |
| Salary | Not stated by the employer |
| Posted | 19 Jun 2026 |
| Last verified | 30 Jul 2026 |
| Source | Employer career page (greenhouse) |
Description
About Pallet
Pallet is building AI Agents to transform logistics — a $12 trillion global industry. We’ve raised $50M from top investors, including General Catalyst, Bessemer Venture Partners, and Bain Capital Ventures. In under two years, we’ve achieved 700% revenue growth and are just getting started.
Our mission is to increase the efficiency of the global supply chain by automating the manual workflows that slow logistics teams down — from scheduling and appointment setting to data entry and load management. Our flagship platform provides end-to-end visibility, control, and optimization, while our newest product, CoPallet, introduces AI Agents that can understand and execute requests in real time, and integrate directly with customer systems.
As logistics providers look to generative AI to drive efficiency, many are turning to Pallet to lead the way. With deep industry expertise and cutting-edge AI capabilities, 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. $160,000 - $200,000 Full-time / Onsite (5 days/week) / ~25% Travel
About The Role
We’re hiring Forward Deployed Product Engineers to deploy production AI agents inside some of the world’s largest logistics companies.
You’ll work directly with customer operators, engineers, and executives to understand how critical workflows actually run. Then you’ll design integrations, debug production systems, and ship AI-powered workflows into environments that are often undocumented, fragmented, and business-critical.
This role sits at the intersection of engineering, product, and customer operations. You’ll own deployments from initial discovery through production launch. You’ll spend time with customers, both remotely and onsite, solving complex operational problems and turning those solutions into reusable product capabilities.
Success in this role is measured by outcomes. Can you get systems live, make them reliable, and solve problems in environments you do not fully control?
Every deployment teaches us something new about how logistics companies operate. The patterns you uncover become product features. The problems you solve for one customer often become capabilities we ship across the platform.
This role is for you if
You enjoy debugging messy real-world systems more than building pristine internal abstractions.
You like working directly with customers and uncovering solutions in ambiguous environments.
You move quickly, make pragmatic decisions, and optimize for outcomes over elegance.
You want ownership of real business impact, not just tickets or isolated services.
You enjoy being the person who figures things out when documentation is incomplete, and the answer isn’t obvious.
This role is likely not for you if
You’re primarily interested in model research or pure ML experimentation.
You prefer highly scoped platform work with limited customer interaction.
You want detailed specifications before starting a project.
You prefer remote-first environments or minimal travel.
What You’ll Do
Reverse engineer undocumented APIs, ERPs, TMS platforms, and internal tools with minimal guidance.
Design and build integrations between customer systems and Pallet’s AI platform.
Debug production failures across distributed systems, authentication layers, data pipelines, and third-party services.
Work onsite with customers to diagnose issues, ship fixes, and drive successful launches.
Own customer deployments end-to-end, from technical discovery through production stability.
Make pragmatic engineering tradeoffs under real-world constraints and tight timelines.
Identify recurring implementation patterns and turn them into reusable product capabilities.
Partner closely with engineering and product teams to shape the roadmap based on customer needs.
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