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Agentic Engineering Manager

Job in Draper, Salt Lake County, Utah, 84020, USA
Listing for: BambooHR
Full Time position
Listed on 2026-10-11
Job specializations:
  • Software Development
    Software Project Mgr/ Lead, AI Engineer (Applied/Software), Software Architect, DevOps
Salary/Wage Range or Industry Benchmark: 180000 - 240000 USD Yearly USD 180000.00 240000.00 YEAR
Job Description & How to Apply Below

Please Note:

This is a Utah-based hybrid position which will require some regular in-office days each week. Additionally, employment with BambooHR is contingent on passing both a background and credit check.

BambooHR is on a mission to set people free to do great work. Our software powers the HR work that small and mid-sized businesses depend on every day. Across engineering, our teams use AI coding agents as the default way to build: specifying intent, delegating execution to agents, verifying and integrating their output, and codifying best practices so each project compounds.

You'll lead the team that makes this possible for everyone else. Your team owns the Agent SDK — the framework other engineering teams build their agents on — along with the core platform underneath it: a RAG pipeline, a sandboxed environment for running agent-generated code, evaluation frameworks, and observability. The specifications, guardrails, and verification standards your team sets don't just govern your own engineers' work, they become the floor every other agentic engineer at BambooHR builds from.

AI tools and capabilities change rapidly. To stay resilient through change, you prioritize curiosity and continual learning, both for yourself and the team you manage, and you actively push new best practices into how your team specifies, delegates, and verifies work. You are accountable for your team's delivery and for the platform's fitness as the foundation other teams depend on: its correctness, its safety, and its reliability at scale.

You will:

Supervisory Responsibilities:

  • Manage 1-2 scrum teams of Software Engineers building the Agent SDK, RAG pipeline, code execution sandbox, evaluations, and observability.
  • Set the bar for how your engineers write agent specifications, delegate execution to AI coding agents, and verify agent-produced output, calibrated to the risk and blast radius of platform-level code
  • Coordinate engineers steering multiple agents in parallel across concurrent platform work streams, keeping engineering judgment and ownership intact
  • Own the scaffolding your team and its downstream consumers delegate through: SDK conventions, agent instructions, evaluation harnesses, and playbooks, and decide which are worth investing in

Duties/Responsibilities:

  • Partner with the teams building on your platform to understand their needs and shape the SDK's roadmap, APIs, and guardrails
  • Demonstrate leadership and patience through regular coaching and mentoring on agentic craft, in individual and team settings
  • Consult with Product Management, UX Designers and Tech Leads to explore and deliver software solutions
  • Manage and maintain your team's execution roadmap, including a grooming pipeline and delivery expectations
  • Show and maintain accountability in team and individual performance (yours and others) in sprint goals and quarterly Objectives and Key Results (OKRs)
  • Contribute to the ongoing improvement of our systems and processes
  • Be a great culture addition and advocate for our company values

What You Need to Get the Job Done

  • 10+ years of experience in software development
  • 2+ years of real-world, demonstrable technical leadership
  • Object-Oriented development experience
  • Experience building or operating platform-level engineering teams — internal SDKs, developer platforms, or infrastructure that other teams build on
  • Experience managing or working alongside engineers who use AI coding agents as the default way to build production software
  • Proficiency in context engineering practices: structuring repo and task context, and setting standards for agent specifications, so agents execute reliably across teams
  • Familiarity with core AI agent infrastructure concepts such as RAG, sandboxed code execution, evaluation frameworks, and…
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