Principle Machine Learning Engineer

Atlassian Corp.; Sydney; Híbrido; AUD 190 000 - 270 000; Tempo integral

Resumo da oferta

Atlassian Corp. is seeking a Principal Machine Learning Engineer to set the technical direction for AI agent quality in Studio.

This hands-on leadership role will define how we measure agent quality and lead efforts to improve performance in production. The role requires shaping evaluation frameworks, balancing cost and accuracy, and guiding multi-team efforts across Sydney and Melbourne with remote options.

Qualificações

• 6+ years in ML/AI engineering with deep expertise in LLMs, NLP, information retrieval or recommendation.
• Proven track record shipping LLM-powered or agentic systems used by real customers, and improving them over time through measurement.
• Strong evaluation and experimental skills; design useful benchmarks, assess label and judge quality, and link offline results to product impact.
• Strong software and data engineering skills; validate designs in code and work with data pipelines, model serving and production observability.
• Strong product judgment: define measurable outcomes and choose between deterministic, classical ML and LLM-based approaches.
• A track record of influencing technical direction across teams and mentoring senior engineers.
• Regular use of AI tools in your own work, turning new research into production results.
• Sound judgment about trade-offs across quality, latency, cost, reliability, privacy and access control.

Responsabilidades

• Own the agent quality architecture; define what to measure and how results roll up.
• Design for cost and accuracy; decide which checks are deterministic or heuristic.
• Architect evaluation and experimentation; define benchmarks and observability for agent behavior.
• Make quality part of shipping; set up gates and drift detection for production traffic.
• Lead the hardest quality problems hands-on, including tool-use reliability and agent routing.
• Lead technical direction across teams; set multi-quarter roadmaps with product and data science.
• Grow ML craft; mentor engineers through design reviews and hands-on work.

Conhecimentos

LLMs

NLP

Information retrieval

Recommendation

Evaluation

Benchmarks

Data pipelines

Model serving

Observability

Mentoring

AI tooling

Research-to-production

Formação académica

Master's or PhD in CS/ML or related field

Descrição da oferta de emprego

Atlassian is seeking a Principal Machine Learning Engineer to set the technical direction for AI agent quality in Studio. This is a hands-on technical leadership role. You'll define how we measure whether agents built on our platform are doing good work, and lead the work to make them better.

Your future team

Studio is where Atlassian customers build, run and manage AI agents under the Rovo brand. Rovo Agents is a model-agnostic cloud agent platform. Teams give an agent instructions, knowledge, tools and skills, then put it to work in Jira and Confluence, inside automation rules, or as an autonomous agent that works on its own.

Because customers build these agents themselves, quality can't be checked once before release. Every agent has its own instructions, data and tools, and any of them can be misconfigured or connected to a tool that fails. We ask three questions of every agent: did it understand and follow the task, did it use its tools correctly and report honestly what it did, and did the result actually help?

The team is software and machine learning engineers based in Sydney and Melbourne, with remote options. We work closely with product, data science and Atlassian's central AI teams. This is applied ML on real agentic systems in production.

What you'll do

Own the agent quality architecture. Define what we measure, how production traffic is sampled, how it is judged, and how results roll up without hiding important failure modes behind one score.

Design for cost and accuracy. Decide which quality questions can be answered with deterministic checks, which need behavioral heuristics, and which need contextual model-based judgment. Build the evaluation system around those decisions.

Architect evaluation and experimentation. Define the benchmarks, datasets, evaluation environments and observability needed to assess agent behavior and task success. Guide teams in building reproducible experiments and calibrated evaluators that inform model, prompt and launch decisions.

Make quality part of how we ship. Set up regression gates and pre-release checks for agent, prompt and model changes, and drift detection on production traffic.

Lead the hardest quality problems hands-on. Examples include agent selection and routing, tool-use reliability, and quality for agents that run inside automations with no user present to give feedback.

Lead technical direction across teams. Set a multi-quarter roadmap with product and data science, partner with central AI teams and model providers, and take accountability for measurable improvements in agent quality.

Grow ML craft. Mentor engineers through design reviews and hands-on work in a team made up mostly of software engineers.

Your background

On the first day, we'll expect you to have:

• 6+ years in ML/AI engineering, with deep expertise in LLMs, NLP, information retrieval or recommendation.
• A proven track record of building and shipping LLM-powered or agentic systems used by real customers, and improving them over time through measurement.
• Strong evaluation and experimental skills. You can design useful benchmarks, assess label and judge quality, recognize leakage and confounding factors, and connect offline results to product impact.
• Strong software and data engineering skills, with the ability to validate designs in code and work deeply with data pipelines, model serving and production observability.
• Strong product judgment: you can find the real problem, define measurable outcomes, and choose between deterministic, classical ML and LLM-based approaches.
• A record of influencing technical direction across multiple teams and mentoring senior engineers.
• Regular use of AI tools in your own work, and the ability to turn new research into production results.
• Sound judgment about trade-offs across quality, latency, cost, reliability, privacy and access control.

It's great, but not required, if you have:

• Experience building agent platforms where customers configure the agents.
• Experience with trajectory-level evaluation of multi-step agents, or prompt and agent optimization.
• Experience in enterprise SaaS.
• A master's or PhD in Computer Science, Machine Learning or a related field.