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.
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.
- 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
- 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
- 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 + 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 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
- Intro Chat with Recruiting - (30 mins)
- Technical Screen + Meet and Greet with an Engineer (60 mins)
- Executive Chat with Bizops Lead or CTO (30-45 mins)
- 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
CompensationThe estimated salary range for this role is $160,000–$200,000, 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.
What We Do
Pallet makes supply chains more efficient. Our AI workforce, CoPallet, handles high-volume tasks like order entry, portal updates, and quoting — all with human-level accuracy and oversight. CoPallet operates your existing tools and processes at a fraction of the cost. We’ve raised over $50M from General Catalyst, Bain Capital Ventures, Activant Capital, and Bessemer Venture Partners. We’re built by a team from the world’s best companies including Google, Rippling, Scale AI, Uber, and other top startups.
Why Work With Us
We move fast, but not at the expense of our people. Growth here means more than hitting metrics — it’s about giving smart, ambitious people the space to stretch, learn, and take on real responsibility. Ownership from day 1: We hire builders. People who want to make decisions, talk to users, and shape how our AI agents evolve.
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