Senior ML Engineer

Overview

Working at Atlassian

Atlassians can choose where they work – whether in an office, from home, or a combination of the two. That way, Atlassians have more control over supporting their family, personal goals, and other priorities. We can hire people in any country where we have a legal entity. Interviews and onboarding are conducted virtually, a part of being a distributed-first company.

Responsibilities

As a Senior Machine Learning Engineer, you will drive work on the development and implementation of the cutting edge machine learning algorithms, collaborating with business, engineering, and analytics teams. You will be tasked to build scalable, reliable and highly performant forecasting algorithms including deep learning based models to forecast company top-line metrics in a granular way. Your daily responsibilities will encompass a broad spectrum of tasks such as designing system and model architectures and model evaluations, and providing guidance to emerging junior ML engineers. Your role is pivotal, stretching beyond these tasks, ensuring AI/ML's transformative potential is realized across our offerings.

Qualifications

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

• Master or PhD in a quantitative subject (Statistics, Mathematics, Operations Research, or relevant work experience)

• 5 years of related industry experience in the forecasting and machine learning domain

• Expertise in Python with and the ability to write performant production-quality code, familiarity with SQL, knowledge of Spark and cloud data environments (e.g. AWS, Databricks)

• Experience designing, building and scaling forecasting and machine learning models in business applications using large amounts of data

• Ability to communicate and explain data science concepts to diverse audiences, craft a compelling story

• Collaborate with a team of experienced scientists to design, implement, and evaluate innovative models, agents, and softwares

• Focus on business practicality and the 80/20 rule; very high bar for output quality, but recognize the business benefit of "having something now" vs "perfection sometime in the future"

• Agile development mindset, appreciating the benefit of constant iteration and improvement

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

• Experience working in a consumer or B2C space for a SaaS product provider, or the enterprise/B2B space

• Experience in developing large scaled forecasting systems and deep learning-based models

• Excelling in solving ambiguous and complex problems, being able to navigate through uncertain situations, breaking down complex challenges into manageable components and developing innovative solutions

Compensation

At Atlassian, we strive to design equitable, explainable, and competitive compensation programs. To support this goal, the baseline of …