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About This Role
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.
### 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
### 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
### 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 Chat with Bizops Lead or CTO (30\-45 mins)
- Final Interview: deep\-dive technical and behavioral interviews (\~4 hours)
### Location
San Francisco: Since we're a small team building 01 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
### Compensation
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.
*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.*
Salary Context
This $160K-$200K range is below the median for AI Agent Developer roles in our dataset (median: $212K across 45 roles with salary data).
View full AI Agent Developer salary data →Role Details
About This Role
AI Agent Developers build autonomous systems that can reason, plan, and take actions. They design multi-step workflows, tool-use frameworks, and orchestration layers that let LLMs interact with external systems. This is the frontier of applied AI engineering.
Agent development is where the most interesting (and hardest) problems in applied AI live right now. Making an LLM answer a question is straightforward. Making it reliably execute a 15-step workflow that involves calling APIs, reading databases, making decisions, and recovering from errors is an unsolved problem. You're building systems that have to work despite the fact that the underlying model is non-deterministic.
Across the 3,824 AI roles we're tracking, AI Agent Developer positions make up 1% of the market. At Pallet, this role fits into their broader AI and engineering organization.
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
What the Work Looks Like
A typical week includes: designing the action space and tool definitions for a new agent use case, debugging why the agent chose the wrong action sequence on a specific input, building evaluation frameworks that test agent reliability across hundreds of scenarios, optimizing the prompt chain for cost and latency, and implementing safety guardrails to prevent the agent from taking destructive actions. The work is equal parts engineering and empirical science.
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
Skills Required
Deep experience with LLM APIs and agent frameworks (LangChain, CrewAI, AutoGen). Strong understanding of prompt engineering, function calling, and error handling for non-deterministic systems. Python is standard. Experience with orchestration patterns, state management, and workflow engines adds significant value.
The best agent developers think like systems engineers. They design for failure modes, build observability into every step, and understand that agent reliability is the product. Expertise in evaluation methodology for non-deterministic systems is the differentiator. Can you measure whether your agent works 'well enough'? Can you find the edge cases where it breaks?
Look for roles that describe specific agent use cases, mention evaluation methodology, and talk about production deployment. Early-stage companies exploring agents can be exciting, but be prepared for ambiguity. The most valuable roles are at companies that have already shipped a v1 and need to make it reliable.
Compensation Benchmarks
AI Agent Developer roles pay a median of $252,000 based on 90 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($180K) sits 29% below the category median. Disclosed range: $160K to $200K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Pallet AI Hiring
Pallet has 1 open AI role right now. They're hiring across AI Agent Developer. Based in New York, NY, US. Compensation range: $200K - $200K.
Location Context
AI roles in New York pay a median of $210,000 across 2,448 tracked positions. That's 5% above the national median.
Career Path
Common paths into AI Agent Developer roles include Software Engineer, LLM Engineer, Prompt Engineer.
From here, career progression typically leads toward AI Architect, Principal Engineer, Head of AI Engineering.
Build agents. That's the portfolio. Take an open-source agent framework, build something that completes a non-trivial multi-step task, evaluate it rigorously, and document what you learned about reliability, cost, and failure modes. The field is new enough that practical experience counts for more than credentials.
What to Expect in Interviews
Interviews focus on systems thinking and reliability engineering. Expect questions about agent architecture: how you'd design a multi-step workflow with error recovery, how you'd evaluate agent performance, and how you'd prevent agents from taking destructive actions. Coding exercises often involve building a simple agent with tool use and evaluating its behavior across different scenarios. Discussion of safety and guardrails is increasingly common.
When evaluating opportunities: Look for roles that describe specific agent use cases, mention evaluation methodology, and talk about production deployment. Early-stage companies exploring agents can be exciting, but be prepared for ambiguity. The most valuable roles are at companies that have already shipped a v1 and need to make it reliable.
AI Hiring Overview
The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
The AI Job Market Today
The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (119) are outnumbered by mid-level (1,813) and senior (1,472) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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