Principal Machine Learning Engineer

As a Principal Machine Learning Engineer at Atlassian, your main responsibilities include tackling complex technical challenges, translating product requirements into executable engineering solutions, mentoring junior members, and contributing to high-performance, customer-centric features within the Jira AI group. You will also actively engage in architectural design and code delivery, ensuring top-notch quality standards.

To qualify for the Principal Machine Learning Engineer role at Atlassian, candidates should have fluency in modern object-oriented programming languages, particularly Java/Kotlin and Python. A solid understanding of the machine learning project lifecycle is essential, as is experience with high-performance RESTful microservices and AWS. While not mandatory, skills in SQL, Databricks, and Amazon Sagemaker are advantageous.

Working as a Principal Machine Learning Engineer at Atlassian offers numerous opportunities for career and personal growth. You will have responsibilities that include leading complex projects, mentoring junior team members, and collaborating on company-wide initiatives, all of which enhance your leadership skills and technical knowledge. Atlassian supports continuous learning and development through various resources and community engagement.

Atlassian enhances work-life balance by offering flexible working options, allowing Principal Machine Learning Engineers to choose between remote work, office-based roles, or a hybrid of the two. This flexibility helps employees align their professional responsibilities with personal commitments effectively, making it easier to manage family and personal goals.

Atlassian provides an extensive array of perks and benefits aimed at supporting employees and their families. Principal Machine Learning Engineers can enjoy health coverage, wellness resources, paid volunteer days, and much more, ensuring that the team has the resources they need to thrive both professionally and personally.

When answering this question, emphasize your familiarity with various stages of the machine learning lifecycle, such as data collection, preprocessing, model training, and deployment. Highlight specific projects where you’ve applied these stages effectively and how they contributed to the project's overall success.

You should mention your proficiency in languages relevant to the position, such as Java/Kotlin and Python, and provide examples of projects where you used these languages for implementing machine learning algorithms and creating scalable solutions. Discuss any frameworks or libraries you have experience with.

Explain your approach to incorporating user feedback throughout the development process. You could discuss specific methods such as user testing, collaborating with product teams, or analyzing user data to tailor the features to meet customer needs effectively.

Use the STAR method (Situation, Task, Action, Result) to structure your answer. Discuss the specific challenge, the steps you took to address it, and the positive outcome that resulted from your actions, showcasing your problem-solving skills and technical expertise.

Detail your exposure to AWS and how you've utilized its services, such as S3, Kinesis, and EKS, in deploying machine learning models. Discuss how you’ve managed resources, scaled applications, and ensured security while working on cloud-based projects.

Talk about your philosophy regarding code quality and how you approach code reviews. Provide examples of how your feedback has improved code quality and team processes and how you encourage best practices within the engineering team.

Discuss the tools you've used for CI/CD processes and explain why you prefer them. Highlight how these tools have enhanced deployment reliability, accelerated development cycles, and improved collaboration among teams in your past projects.

Share your experiences designing and implementing distributed systems, focusing on their architecture, scalability, and performance. Provide examples of specific projects or challenges you've faced while ensuring their effectiveness in real-world applications.

Discuss your approach to mentorship, emphasizing the significance of nurturing talent and fostering a collaborative environment. You might share specific methods you adopt to help junior members grow in their skills and confidence, such as pair programming, regular check-ins, and sharing resources.

Articulate your enthusiasm for Atlassian's mission and culture. Mention things like their focus on collaboration, innovation, and how you relate to their commitment to personal development and work-life balance, emphasizing how these align with your career goals.