Engineering | San Francisco, United States | Remote, Remote
Working at AtlassianAtlassians can choose where they work whether in an office, from home, or a combination of the two. That way, Atlassians have more control over supporting their family, personal goals, and other priorities. We can hire people in any country where we have a legal entity.
Your future teamAtlassian is betting that the next era of teamwork will be shaped by people and AI agents working together, with Rovo as the intelligent teammate that understands an organization's context and helps move work forward. Turning Rovo's reach into lasting customer value, experiences people trust, return to, and rely on, is central to Atlassian's ambition to lead in AI and to our next chapter of growth. Getting quality right is what turns that ambition into customer success, deeper adoption, and durable business impact.
You'll join the Rovo Chat Quality team within Atlassian's Rovo + AI organization. We are the team that turns "make Rovo better" into something we can measure, understand, and ship. We build the evaluation systems, datasets, quality signals, and production ML capabilities that reveal where Rovo succeeds, where it falls short, and which changes make it meaningfully more useful to customers.
What you'll doAs a Principal Machine Learning Engineer, you'll tackle the open-ended technical problems that determine whether customers trust Rovo with increasingly complex and consequential work. You'll remain deeply hands-on while setting direction across teams: driving advances in models, agents, and evaluation and turning them into measurable improvements for millions of active users.
- Set the technical vision for how Atlassian measures and improves Rovo Chat, translating qualities such as helpfulness, correctness, judgment, and reliability into rigorous evaluation strategies.
- Architect and build production ML and evaluation systems that operate at scale, including datasets, simulations, automated judges, regression suites, experimentation platforms, and online quality signals.
- Advance the state of Rovo's agentic capabilities across reasoning, planning, tool selection and use, action correctness, retrieval, memory, and long-horizon execution.
- Close the loop between evaluation and product impact: identify systemic failure modes, develop interventions, ship them to production, and establish whether they improved customer outcomes.
- Make high-leverage technical decisions across model choice, prompting, fine-tuning, data strategy, system architecture, and inference; balancing quality with latency, reliability, safety, and cost.
- Lead ambitious, cross-organizational initiatives with ML, engineering, product, analytics, research, and design partners, creating clarity and momentum where neither the problem nor the solution is well defined.
- Establish reusable methods, systems, and quality standards that raise the bar not only for Rovo Chat, but for AI-powered experiences across Atlassian.
- Multiply the impact of other engineers through technical leadership, design reviews, mentorship, and a compelling long-term vision for how we build and evaluate world-class AI products.
Your backgroundWe expect you to bring:
- A track record of architecting and shipping large-scale ML or AI systems that deliver measurable customer and business impact.
- Deep experience with LLM-powered products, agentic systems, model evaluation, experimentation, and dataset development.
- Strong production engineering skills and the ability to remain hands-on in a modern ML stack.
- Principal-level technical leadership: setting direction, influencing across teams, and leading complex initiatives from ambiguity to production.
- Excellent product and technical judgment, including balancing quality, speed, reliability, safety, latency, and cost.
- The ability to communicate complex ideas clearly and raise the effectiveness of engineers around you through mentorship and technical leadership.
At Atlassian, we strive to design equitable, explainable, and competitive compensation programs. We follow consistent hiring practices and account for each candidate's skills, knowledge, and experience when setting base pay within the range.
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