Staff Engineer, Karlo Platform at Kargo

Job description

Kargo creates powerful moments of connection between brands and consumers to build businesses. Every day, our dynamic teams work to radically raise the bar on what agentic AI, CTV, eCommerce, social, and mobile can do to deliver unique ad experiences across the world’s most premium platforms. Taking a Creative Science approach to all we do, we continuously innovate solutions that outperform industry benchmarks and client expectations. Kargo is growing rapidly and currently has offices in NYC, Chicago, LA, Dallas, Sydney, Auckland, London and Waterford, Ireland. Who We Hire Techies who want to build the future. Creatives who want to design it better. Communicators to win business. Collaborators to build it. Data pros who turn numbers into insights. Product builders who turn ideas into innovations. Anyone eager to be on a team that doesn’t stop to ask what’s next, because they’re already building it. The Opportunity Karlo is Kargo's agentic ad-buying platform, where customers use conversational AI agents to build campaigns, creatives, targeting strategies, and analytics. As an AI Platform Engineer on the Karlo Platform team, you'll own the shared infrastructure every agent runs on — observability, model evaluation, the LangGraph runtime, checkpointing and context memory management, shared services, and the developer experience tying it all together. Your goal is to make the platform invisible to the agent teams building on it: so reliable, scalable, and well-documented that they can focus entirely on their agents. The Daily To-Do Own and harden the core LangGraph-based agent hosting infrastructure so every agent team can build and deploy with confidence — no undocumented behaviors, no surprise failures Design and deliver a shared evaluation framework that lets agent teams validate correctness, measure quality, and catch regressions before they reach customers Ship production-ready shared services — authentication, billing, and integration layers including Salesforce and CXP — that agent teams consume cleanly instead of rebuilding Own checkpointing and agent context memory — state persistence, history compaction, and retrieval of relevant prior context across long-running, multi-turn conversations Instrument the platform end-to-end: every agent, every session, every failure, with tracing across LLM calls plus latency, cost, and failure-mode visibility Build the SDKs, documentation, and onboarding tooling that cut a new agent team's ramp time from weeks to days Partner closely with agent teams to translate their needs into platform capabilities, and measurably reduce the infrastructure overhead they carry today Qualifications 7–10 years of software engineering experience, including at least 2 years building production AI or agentic systems — shipped and operated, not prototyped Deep hands-on experience with LangGraph in production: graph construction, node and edge design, conditional routing, state management, and agent lifecycle management; plus the broader LangChain ecosystem and LangSmith for tracing, evaluation, and dataset management Command of agentic design patterns — ReAct, Plan-and-Execute, multi-agent orchestration, human-in-the-loop workflows, and tool use — with experience operating multi-agent systems and state handoff across agent boundaries Strong LLM foundations: prompt engineering, context window management, memory architectures for multi-turn agents, token and cost optimization, RAG architectures (vector stores, embedding pipelines, retrieval, reranking), and model selection tradeoffs Experience with LLM evaluation methodologies and production observability for AI systems, including the ability to debug non-deterministic agent behavior methodically Strong backend fundamentals across distributed systems, API design, and event-driven architecture; TypeScript required and Golang a strong plus; cloud infrastructure (AWS, GCP, or Azure), containerization, and CI/CD Experience building shared platform infrastructure consumed by multiple teams, with a track record of improving developer experience through SDKs, documentation, or internal tooling; B.S. or higher in Computer Science, Engineering, Mathematics, or equivalent practical experience; AdTech or programmatic experience a plus In accordance with applicable federal, state, and local pay transparency laws, the anticipated base salary range for this position is listed below. In addition to base salary, this role is eligible for variable compensation — either the Kargo Sales Incentive Plan (sales roles) or an annual discretionary bonus (all other roles). Actual compensation may vary based on factors such as geographic location, work experience, education, and skills. U.S Salary Range $200,000 — $220,000 USD What We're Proud Of AdAge Best Places to Work ThinkLA Partner of the Year Built In Best Places to Work Cynopsis 2025 Top Women in Media - Jeannine Shao Collins Martech Breakthrough Awards - Best Overall Adtech Company Digiday Media Awards Best Event Cynopsis Media Impact Awards-Best CTV Platform Martech Breakthrough Awards-CTV Innovation Adweek Media Plan of the Year Awards - Best Use of Insights Following Our Lead Big Picture: kargo.com The Latest: Instagram (@kargo.hq) and LinkedIn (Kargo)

Requirements

Must-haves

The Daily To-Do
Own and harden the core LangGraph-based agent hosting infrastructure so every agent team can build and deploy with confidence — no undocumented behaviors, no surprise failures
Design and deliver a shared evaluation framework that lets agent teams validate correctness, measure quality, and catch regressions before they reach customers
Ship production-ready shared services — authentication, billing, and integration layers including Salesforce and CXP — that agent teams consume cleanly instead of rebuilding
Own checkpointing and agent context memory — state persistence, history compaction, and retrieval of relevant prior context across long-running, multi-turn conversations
Instrument the platform end-to-end: every agent, every session, every failure, with tracing across LLM calls plus latency, cost, and failure-mode visibility
Build the SDKs, documentation, and onboarding tooling that cut a new agent team's ramp time from weeks to days
Partner closely with agent teams to translate their needs into platform capabilities, and measurably reduce the infrastructure overhead they carry today
7–10 years of software engineering experience, including at least 2 years building production AI or agentic systems — shipped and operated, not prototyped
Command of agentic design patterns — ReAct, Plan-and-Execute, multi-agent orchestration, human-in-the-loop workflows, and tool use — with experience operating multi-agent systems and state handoff across agent boundaries
Experience with LLM evaluation methodologies and production observability for AI systems, including the ability to debug non-deterministic agent behavior methodically
Strong backend fundamentals across distributed systems, API design, and event-driven architecture; TypeScript required and Golang a strong plus; cloud infrastructure (AWS, GCP, or Azure), containerization, and CI/CD
AdAge Best Places to Work
ThinkLA Partner of the Year
Built In Best Places to Work
Cynopsis 2025 Top Women in Media - Jeannine Shao Collins
Martech Breakthrough Awards - Best Overall Adtech Company
Digiday Media Awards Best Event
Cynopsis Media Impact Awards-Best CTV Platform
Martech Breakthrough Awards-CTV Innovation
Adweek Media Plan of the Year Awards - Best Use of Insights
Following Our Lead
Big Picture: kargo.com
The Latest: Instagram (@kargo.hq) and LinkedIn (Kargo)

Nice-to-haves

None flagged.

Application prep

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Keywords to mirror

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