Overview
The Developer AI team is working on products and experiences that apply AI to redefine software development, removing toil and grunt work for developers and radically improving developer productivity. This work is on the leading edge of technical capability and will have huge industry impact.
Compensation
At Atlassian, we strive to design equitable, explainable, and competitive compensation programs. To support this goal, the baseline of our range is higher than that of the typical market range, but in turn we expect to hire most candidates near this baseline. Base pay within the range is ultimately determined by a candidate's skills, expertise, or experience. In the United States, we have three geographic pay zones. For this role, our current base pay ranges for new hires in each zone are:
Zone A: $199,400 - $265,800
Zone B: $179,400 - $239,200
Zone C: $165,500 - $220,600
This role may also be eligible for benefits, bonuses, commissions, and equity.
Responsibilities
As a Senior Machine Learning engineer, you will work on the development and implementation of cutting-edge machine learning algorithms, training sophisticated models, collaborating with product, engineering, and analytics teams, to build transformative developer AI products and services. Your daily responsibilities will encompass a broad spectrum of tasks such as designing system and model architectures, conducting rigorous experimentation and model evaluations, and providing guidance to junior ML engineers. Your role is pivotal, stretching beyond these tasks, ensuring AI's transformative potential is realized across our offerings.
Qualifications
On the first day, we'll expect you to have
Bachelor's or Master's degree (preferably a Computer Science degree or equivalent experience)
3+ years of related industry experience in the data science domain
Expertise in Python or Java with the ability to write performant production-quality code, familiarity with SQL, knowledge of Spark and cloud data environments (e.g. AWS, Databricks)
Experience building and scaling 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
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 deep learning-based models and working on LLM-related applications
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
#J-18808-Ljbffr
