Principle Machine Learning Engineer

As a Principal Machine Learning Engineer at Atlassian, your primary responsibilities include designing advanced machine learning algorithms, constructing the necessary infrastructure for AI initiatives, and collaborating with product, engineering, and analytics teams to embed AI functionalities into our products. You’ll also conduct rigorous experimentation and model evaluations while offering mentorship to emerging ML engineers.

To qualify for the Principal Machine Learning Engineer role at Atlassian, candidates should possess strong expertise in machine learning and data science, along with a solid understanding of algorithm design and model evaluation techniques. A proven track record of collaborating on complex projects and providing guidance to junior members in the field is also essential.

The salary for a Principal Machine Learning Engineer at Atlassian varies depending on geographic location, with ranges as follows: Zone A ($229,200 - $305,600), Zone B ($206,300 - $275,100), and Zone C ($190,300 - $253,700). These figures indicate the baseline pay, reflecting our commitment to equitable compensation.

Atlassian provides a comprehensive range of benefits for its employees, including health coverage, paid volunteer days, wellness resources, and support for your family. Additionally, performance bonuses and equity options may be part of your compensation package, further enhancing your overall employment experience.

The Central AI organization at Atlassian plays a crucial role in driving AI innovation across all products and platforms. It ensures a unified AI infrastructure is created so that various departments can collaborate effectively, avoiding siloed efforts. The team is dedicated to integrating AI-driven solutions that enhance user experience and provide core shared functionalities like search and knowledge discovery.

In your response, focus on specific projects where you have designed and implemented machine learning models. Highlight the algorithms you used, the data sets, and any challenges you overcame during the process. Explain how these projects resulted in meaningful outcomes for your team or company.

Explain your methodology for model evaluation, including metrics you prioritize like accuracy, precision, recall, and F1 score. Discuss any tools or libraries you've used for this purpose and why you believe effective model evaluation is critical for successful deployment.

Discuss specific resources you utilize to stay informed, such as research papers, conferences, webinars, or online courses. Mention any influential thought leaders in ML/AI whom you follow and how their insights might shape your work.

Share an experience that clearly illustrates how you guided a less experienced engineer through challenges in a project. Focus on the strategies you employed to facilitate learning and growth, and how this mentorship benefitted the overall project outcome.

Discuss various feature selection techniques you have utilized, such as recursive feature elimination, random forests, or PCA. Explain how these methods improve model performance and stability, providing examples from your experience.

Choose a concept, such as overfitting or neural networks, and break it down into simple terms. Use analogies that relate to everyday life to ensure clarity, showcasing your communication skills and ability to explain technical details to non-specialists.

Describe your approach to data cleaning, how you assess the impact of missing data, and the methods you use, such as imputation or utilizing algorithms that handle missing values. Emphasize the importance of high-quality data in machine learning success.

Discuss your perspective on the ethical considerations in AI, touching on biases in data, transparency, and accountability. Explain how you advocate for ethical practices within ML projects and your experience addressing these challenges.

If applicable, share your experience with federated learning environments, detailing any techniques or frameworks you utilized. If not, explain your understanding of distributed learning and its benefits, reflecting your awareness of evolving machine learning trends.

Outline your knowledge of the entire model deployment pipeline, from development to production. Include tools and platforms you’ve used, discuss challenges faced during deployment, and how you ensured model performance post-deployment.