Job description

LeanData helps the world's fastest-growing companies automate, simplify, and accelerate revenue. We are looking for a Staff Engineer with experience building Agentic AI systems to help design and ship production multi-agent systems at the core of LeanData's new platform of autonomous agents for go-to-market teams. This is a Staff-level individual-contributor role with founding-level ownership. You would work closely with engineering and product leadership to define and build the architecture of a sophisticated multi-agent system able to reason over go-to-market data and act reliably at enterprise scale. This a Senior technical position — any engineer can tell a coding agent to build things; we are looking for people who know how to design and deliver production agentic systems that actually work safely when connected to customers' live data systems, and who give deep thought to best-practices for building production agentic systems. We know just about every candidate for this position has built agentic systems — we are going to ask how you made sure the systems you built work. This role reports to the Director of Engineering - AI and Machine Learning, and is based in our Santa Clara, CA office. You are required to be in office Mondays and Wednesdays each week. What you'll be doing - Designing and building multi-agent systems that are able to work with complex, dirty data structures and varying policies to safely write to mission-critical customer data stores - Establish patterns and best practices that help the whole team build better and faster - Agent control loops and planning, the orchestration graphs and tools they call, and the coordination, handoffs, state, and durability across agents - Build the safe write-back path that applies agent decisions to a customer's live systems without corrupting data - Measure, test, and evaluate everything you ship: build the eval cases, rubrics, and regression tests that prove a change made the agent better - Guardrails for agentic systems - Integrate frontier LLMs behind a model-agnostic abstraction with routing by task, cost, and latency; own the cost, latency, and reliability of your surface, and create the patterns that let the team build agents faster Requirements - 4+ years building production systems, including 2+ years shipping LLM-powered or agentic systems - You have shipped customer-facing LLM or agent systems that real users depend on, and can explain how they failed and how you fixed them - High agency: you scope, prioritize, and ship without waiting for permission - Ability to reason, make decisions, and act in the face of ambiguity - Strong, current Python (TypeScript or Go a plus) - Hands-on with a modern agent framework / orchestration (e.g. LangGraph or agent SDKs), tool/function-calling, structured outputs, retrieval (RAG), and context/memory management - Strong systems-engineering fundamentals (concurrency, distributed systems, statefulness, latency and cost at scale), plus skill debugging deep, non-deterministic failures in multi-step agent traces Bonus points if you have - Experience with Salesforce APIs (Bulk 2.0, Composite, Pub/Sub) or another large, messy enterprise data source - A founder or founding-engineer background, or contributions to open-source agent or LLM tooling Compensation The salary for this role will be between $160,000 and $200,000 base, plus equity.

Requirements

Must-haves

LeanData helps the world's fastest-growing companies automate, simplify, and accelerate revenue.
We are looking for a Staff Engineer with experience building Agentic AI systems to help design and ship production multi-agent systems at the core of LeanData's new platform of autonomous agents for go-to-market teams.
What you'll be doing
Designing and building multi-agent systems that are able to work with complex, dirty data structures and varying policies to safely write to mission-critical customer data stores
Establish patterns and best practices that help the whole team build better and faster
Agent control loops and planning, the orchestration graphs and tools they call, and the coordination, handoffs, state, and durability across agents
Build the safe write-back path that applies agent decisions to a customer's live systems without corrupting data
Measure, test, and evaluate everything you ship: build the eval cases, rubrics, and regression tests that prove a change made the agent better
Guardrails for agentic systems
Integrate frontier LLMs behind a model-agnostic abstraction with routing by task, cost, and latency; own the cost, latency, and reliability of your surface, and create the patterns that let the team build agents faster
4+ years building production systems, including 2+ years shipping LLM-powered or agentic systems
You have shipped customer-facing LLM or agent systems that real users depend on, and can explain how they failed and how you fixed them
High agency: you scope, prioritize, and ship without waiting for permission
Ability to reason, make decisions, and act in the face of ambiguity
Hands-on with a modern agent framework / orchestration (e.g. LangGraph or agent SDKs), tool/function-calling, structured outputs, retrieval (RAG), and context/memory management
Strong systems-engineering fundamentals (concurrency, distributed systems, statefulness, latency and cost at scale), plus skill debugging deep, non-deterministic failures in multi-step agent traces
Experience with Salesforce APIs (Bulk 2.0, Composite, Pub/Sub) or another large, messy enterprise data source
A founder or founding-engineer background, or contributions to open-source agent or LLM tooling

Nice-to-haves

Strong, current Python (TypeScript or Go a plus)
Bonus points if you have

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aigomachine learningllmpythontypescript

Platform: ashby
Effort: quick