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About This Role
Traba is the AI operating layer for the industrial supply chain.
We started in workforce—temp staffing, the biggest operational pain point for the manufacturing and logistics customers we serve—and used it to embed ourselves inside their daily operations and create a far better customer experience through technology.
Now those same customers are pulling us beyond staffing into the broader operational workflows that run their facilities. That foundation gave us proprietary data from millions of shifts and deep enterprise relationships.
But our edge is more than data: by connecting to the systems running across every facility and activating the workers already on our platform to execute against them, we are building applied AI that drives real productivity gains and transforms how the global supply chain operates at scale.
We are backed by Founders Fund, Khosla Ventures, and General Catalyst.
You'll build the AI agents themselves—the harnesses, evals, orchestration, and model strategy the rest of Traba's product runs on. We're seeking an entrepreneurial Senior AI Agent Engineer to join as a founding member of the Agents team and build the next layer of Traba's product—an agentic platform that synthesizes the data flowing through our marketplace and operates autonomously inside our customers' supply chain workflows. (Our Applied AI team puts these agents to work across the product; you own the agents and infrastructure they depend on.) You'll embed deeply in our customers' operations, design and ship production agent harnesses on top of frontier models, and turn what you learn in the field into durable product. You will partner closely with our CTO, product, and our operators to take 0\-to\-1 ideas to scaled deployments.
About You
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- Builder with an AI operator's instinct. You've shipped real product on top of LLMs—not just chat wrappers. You've designed agent harnesses, structured tools, written evals, and tuned prompts against production traces. You think in capability, reliability, and unit economics—not just whether the model says the right thing.
- Domain\-immersed. You enjoy time with the people who do the work—learning an industry's vocabulary, edge cases, and operational tempo—and let that shape what you build.
- High\-agency in ambiguity. Dropped into a fuzzy customer problem with a half\-formed hypothesis and a deadline, you scope, build, evaluate, and ship without waiting for a spec.
- Sweat both ends of the stack. You move between prompt iteration, eval design, backend services, and customer\-facing UI in the same week, and you care about evals that catch regressions and traces that are easy to debug.
You Will
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- Embed with customers and operators to understand how supply chains run today—then design and ship agents that take meaningful work off their plate.
- Build production agent systems on frontier LLMs: tool use, sub\-agents, retrieval, structured outputs, MCP servers, and the orchestration that ties them together.
- Own evaluation as a first\-class discipline—datasets from real traces, rubrics and graders, experiments, and improvements you can prove move the needle.
- Architect the data, services, and APIs the agent layer depends on—integrating our internal systems with customers' WMS, TMS, and ERP environments.
- Codify repeatable deployment patterns so each new customer rollout is faster than the last.
You Have
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- 5\+ years of software engineering, with 1\+ year shipping LLM\- or agent\-based features into production.
- Strong in Python and/or TypeScript/Node.js, and comfortable designing APIs, distributed systems, and data models in PostgreSQL.
- Hands\-on with the modern agent stack: production\-scale prompt engineering, evaluation frameworks, orchestration patterns, and frontier model APIs.
- A track record of building in fast, ambiguous environments—ideally at a vertical AI, AI\-agent, forward\-deployed, or data\-product company.
- Excellent written and verbal communication—you can run a customer workshop in the morning and write a clean design doc in the afternoon.
Benefits
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- Start\-up equity
- Competitive Salary
- 100% Paid health, dental \& vision coverage
- Dinner Provided via DoorDash, free DashPass \& stocked kitchen for NY employees
- Commuter benefit
- Gympass Benefit
- ✚✚ Additional: One Medical Membership, Gympass, HSA via Optum, Talkspace, HealthAdvocate, Teledoc Health
Salary Range Details
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The compensation range for this position is set between $200,000 and $240,000, reflecting our market analysis and other relevant considerations. However, exceptions may be made for candidates with qualifications that significantly differ from those outlined in the job description.
Equal Opportunity Employer
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Traba is dedicated to promoting Equal Opportunity employment practices. We evaluate all applicants without discrimination based on race, color, religion, creed, national origin, age, sex, gender, marital status, sexual orientation and identity, genetic information, veteran status, citizenship, or any other characteristics that are legally protected by local, state, or federal regulations. We encourage applicants from a variety of backgrounds, experiences, and skill sets.
Our Values
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Dream Big \- We are on a path to change the world for the better. We create and communicate a bold direction that inspires a life\-changing vision. We don’t sacrifice long\-term value for short\-term results.
Olympian’s Work Ethic \- Changing the world never comes easy. We work harder, longer, and smarter, not just two out of three. We put everything we have on the field.
Growth Mindset \- We confront the toughest challenges head\-on and persevere. Sometimes we fail, but we brush ourselves off, adapt, learn, and push forward with resilience.
Customer Obsession \- We go the extra mile for our workers and businesses. We remain focused on delivering high\-quality products and services that solve this massive and overlooked industries’ problems
Job Applicant Privacy Notice
Compensation Range: $200K \- $240K
Salary Context
This $200K-$240K range is above the median for AI Agent Developer roles in our dataset (median: $188K across 26 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 3,708 AI roles we're tracking, AI Agent Developer positions make up 1% of the market. At Traba, 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 $238,500 based on 58 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($220K) sits 8% below the category median. Disclosed range: $200K to $240K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Traba AI Hiring
Traba has 7 open AI roles right now. They're hiring across AI Agent Developer, AI Software Engineer. Positions span New York, NY, US, San Francisco, CA, US. Compensation range: $200K - $300K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 tracked positions.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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.
Frequently Asked Questions
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