Principal Machine Learning System Engineer

Atlassian•Austin, TX

3h•$174,051 - $273,775•Hybrid

About The Position

As a Principal Machine Learning Systems Engineer on the Search Platform team, you will set the technical direction for search infrastructure that powers Rovo, Jira, Confluence, and the broader Atlassian AI platform. You will operate with broad organizational influence, driving outcomes across teams and functions without relying on positional authority. This role involves defining and championing the multi-year technical roadmap for search serving, vector infrastructure, and agentic retrieval. You will identify architectural inflection points and build organizational consensus. You will also drive high-impact initiatives across teams, influence peers and stakeholders, and own the technical strategy for retrieval systems that ground Rovo agents and AI workflows. Additionally, you will set the standard for production ML serving, cost discipline, and reliability, and raise the technical bar across the organization through code review, design feedback, and mentorship.

Requirements

Set the technical direction for search infrastructure that powers Rovo, Jira, Confluence, and the broader Atlassian AI platform.
Operate with broad organizational influence, driving outcomes across teams and functions without relying on positional authority.
Define and champion the multi-year technical roadmap for search serving, vector infrastructure, and agentic retrieval.
Identify architectural inflection points — such as the evolution from keyword to semantic search, or the shift toward retrieval-for-agents — and build organizational consensus around the right path forward.
Earn alignment through technical credibility, clear reasoning, and the ability to make complex tradeoffs legible to engineers, product managers, and senior leadership alike.
Drive high-impact initiatives across teams that do not report to you.
Influence peers, partner teams, and stakeholders by framing problems compellingly, co-authoring shared technical strategies, and creating forums — deep dives, design reviews, working groups — where the best ideas win on merit.
Navigate ambiguity and competing priorities across the Search Platform, ML Platform, AI Gateway, and Rovo product teams to keep critical cross-functional programs on track.
Own the technical strategy for retrieval systems that ground Rovo agents and AI workflows, including personalized indexes, multi-step retrieval pipelines, and RAG infrastructure at scale.
Anticipate how agentic use cases will stress existing search architecture and proactively design for those requirements before they become blockers.
Set the standard for production ML serving, cost discipline, and reliability across the search stack.
Drive FinOps-oriented decisions — such as vector cluster right-sizing and query embedding caching — that deliver material infrastructure savings without sacrificing SLO targets.
Raise the technical bar across the organization through code review, design feedback, and sponsoring the growth of senior engineers toward principal-level impact.

Responsibilities

Define and champion the multi-year technical roadmap for search serving, vector infrastructure, and agentic retrieval.
Identify architectural inflection points and build organizational consensus around the right path forward.
Drive high-impact initiatives across teams that do not report to you.
Influence peers, partner teams, and stakeholders by framing problems compellingly, co-authoring shared technical strategies, and creating forums where the best ideas win on merit.
Navigate ambiguity and competing priorities across the Search Platform, ML Platform, AI Gateway, and Rovo product teams to keep critical cross-functional programs on track.
Own the technical strategy for retrieval systems that ground Rovo agents and AI workflows, including personalized indexes, multi-step retrieval pipelines, and RAG infrastructure at scale.
Anticipate how agentic use cases will stress existing search architecture and proactively design for those requirements before they become blockers.
Set the standard for production ML serving, cost discipline, and reliability across the search stack.
Drive FinOps-oriented decisions that deliver material infrastructure savings without sacrificing SLO targets.
Raise the technical bar across the organization through code review, design feedback, and sponsoring the growth of senior engineers toward principal-level impact.