Note: The job is a remote job and is open to candidates in USA. Atlassian develops collaboration software designed to help teams unleash their potential. The Senior Machine Learning Engineer will build and scale production machine learning and evaluation systems for Rovo Chat, advance agentic AI capabilities, and connect evaluation results to measurable customer outcomes. The role also provides technical leadership across teams through architecture decisions, cross-functional initiatives, mentorship, and quality standards.
Responsibilities
- 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
Skills
- • 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
- • 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
Benefits
- Atlassians can choose where they work – whether in an office, from home, or a combination of the two.
- This role may also be eligible for benefits, bonuses, commissions, and equity.
- Health and wellbeing resources
- Paid volunteer days
Company Overview
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