Member of Technical Staff, Agent Delivery

$160,000 - $200,000

*Full-time / Onsite SF or NY (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.

Who You Are

• 2–5 years of experience building and owning production systems in high-growth startups or similarly demanding environments.
• Strong experience working with APIs, integrations, authentication systems, and event-driven architectures.
• Proven ability to debug complex systems in production.
• Comfortable operating in ambiguous environments where documentation is incomplete, and requirements evolve quickly.
• Strong communication skills and the ability to build trust with technical and non-technical stakeholders.
• Experience working directly with customers is a strong plus,

Our Tech Stack

• 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

The 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.