Senior Machine Learning Engineer - job 1 of 2

As a Senior Machine Learning Engineer at Atlassian, your main responsibilities will involve innovating core intelligence solutions within our DevAI products, including LLM integration and prompt engineering. You will also develop end-to-end AI features, solve complex problems, collaborate with cross-functional teams, and communicate technical details effectively to enhance product innovation.

At Atlassian, to qualify for the Senior Machine Learning Engineer position, candidates should have demonstrated expertise in machine learning, AI applications, and relevant technologies. The role requires strong problem-solving skills, the ability to collaborate with diverse teams, and a commitment to continuous learning and application of the latest research in the field.

At Atlassian, we believe in continuous learning as a vital part of career growth. Senior Machine Learning Engineers will have access to resources for ongoing education and professional development. Additionally, the collaborative environment allows you to work alongside leading experts, enhancing your skills and knowledge through shared experiences.

The Senior Machine Learning Engineer role at Atlassian includes a wide range of benefits, including competitive compensation, health and wellbeing resources, paid volunteer days, and support for work-life balance. We prioritize the wellbeing of our employees, ensuring they have the necessary support to thrive both personally and professionally.

At Atlassian, the compensation for Senior Machine Learning Engineers varies by location. In the United States, the base pay range starts from $165,500 to $265,800, depending on the geographic pay zone. This role may also be eligible for benefits, bonuses, commissions, and equity, making it a competitive opportunity in the industry.

When answering this question, mention specific frameworks such as TensorFlow, PyTorch, or Scikit-learn that you've used in projects. Emphasize how they've contributed to your work and the impact they’ve had on achieving project goals at your previous jobs.

Share a specific project where you faced challenges, such as data quality issues or algorithm selection. Explain the steps you took to overcome these obstacles, showcasing your problem-solving skills, and highlight the results or learnings from the project.

Discuss your strategies for continuous learning, whether it's attending conferences, participating in online courses, or reading recent research papers. Highlight specific sources of information and how you apply new knowledge to your work to enhance your skills and product offerings.

Explain your approach to feature engineering, giving examples of techniques like normalization, one-hot encoding, or using domain knowledge to create new features. Discuss how these techniques have led to better model performance in your previous projects.

Detail your process for model evaluation, mentioning key metrics like accuracy, precision, recall, and F1 score, as well as techniques like cross-validation. Discuss how you use these metrics to iterate on and improve your models.

Talk about your experience in taking models from development to deployment, including the tools and technologies you've used. Emphasize any challenges faced during deployment and how you ensured models perform effectively post-deployment.

Clarify the importance of data in machine learning, covering aspects like data quality, quantity, and variety. Discuss how good data practices have led to successful project outcomes in your experience.

Share a specific instance where a model didn’t meet your expectations. Discuss your systematic approach to troubleshooting, including examining data, feature selection, and model assumptions to learn from the experience and improve.

Describe your methods for explaining complex technical concepts clearly and concisely. You'll want to emphasize your ability to use analogies, visuals, and simplified language to ensure that all team members can understand your work.

Illustrate your collaborative style by providing examples of successful projects where teamwork was essential. Share techniques you use to facilitate communication, build trust, and encourage shared problem-solving.