Principal Machine Learning Engineer
Atlassian
Principal Machine Learning Engineer
Job Description
Principal Machine Learning Engineer
📋 Job Overview
The Principal Machine Learning Engineer at Atlassian will tackle complex problems, work closely with cross-functional teams, and deliver data-driven solutions within the Jira AI group. This role involves leading technical design, architecture, and code delivery, while mentoring junior team members.
📍 Location: Multiple Locations, Global
🏢 Category: Engineering
📅 Posted: 2025-12-11 03:58 PM
🎯 Key Responsibilities
- Tackle the largest and most complex problems in the team, from technical design to launch
- Work closely with Product, Engineering and Design leads in Jira AI, and translate their requirements into solid engineering deliverables, delegating work to the teams
- Deliver solutions that are used by other teams and products
- Follow a Product Engineer mindset by building features that are data-driven and customer-centric, fostering that culture within the Jira AI group
- Exceptional problem solving ability using ML, AI and core software engineering
- Routinely tackle complex architecture challenges and define architectural standards
- Actively contribute to the code delivery through leading code reviews & documentation, direct contribution and fixing complex bugs in high-risk surface areas
- Expertise in data analysis, statistical methods, and logical reasoning to inform data-driven decision-making
- Partner across engineering teams to take on company-wide initiatives spanning multiple projects
- Mentor junior members on the team
✅ Required Qualifications
- Fluency in at least one modern object-oriented programming language (preferably Java/Kotlin and Python)
- Understanding of Machine Learning project lifecycle and tools
- Experience in architecting and implementing high-performance RESTful microservices
- Experience building and operating large scale distributed systems using Amazon Web Services (S3, Kinesis, Cloud Formation, EKS, AWS Security and Networking)
- Experience with leveraging LLMs effectively and optimizing model usage on GPUs
- Experience with Databricks or Apache Spark
- Experience with Continuous Delivery and Continuous Integration
⭐ Preferred Qualifications
- Expert-level SQL knowledge, query tuning, schema design, and ETL processes
- Experience with Amazon Sagemaker
- Experience with scaling and deploying Machine Learning models
🛠️ Required Skills
- Java
- Kotlin
- Python
- Machine Learning
- RESTful microservices
- Amazon Web Services (S3, Kinesis, Cloud Formation, EKS, AWS Security and Networking)
- LLMs
- GPUs
- Databricks
- Apache Spark
- Continuous Delivery
- Continuous Integration
- Data analysis
- Statistical methods
- Logical reasoning
- Problem solving
- Architecture
- Code reviews
- Documentation
- Mentoring
🎁 Benefits & Perks
- Flexibility to choose where to work - in an office, from home, or a combination
- Ability to support family, personal goals, and other priorities
- Opportunity to work in any country where Atlassian has a legal entity
- Virtual interviews and onboarding as part of being a distributed-first company
Locations
- Multiple Locations, Global
Salary
Estimated Salary Rangemedium confidence
200,000 - 300,000 USD / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Javaintermediate
- Kotlinintermediate
- Pythonintermediate
- Machine Learningintermediate
- RESTful microservicesintermediate
- Amazon Web Services (S3, Kinesis, Cloud Formation, EKS, AWS Security and Networking)intermediate
- LLMsintermediate
- GPUsintermediate
- Databricksintermediate
- Apache Sparkintermediate
- Continuous Deliveryintermediate
- Continuous Integrationintermediate
- Data analysisintermediate
- Statistical methodsintermediate
- Logical reasoningintermediate
- Problem solvingintermediate
- Architectureintermediate
- Code reviewsintermediate
- Documentationintermediate
- Mentoringintermediate
Required Qualifications
- Fluency in at least one modern object-oriented programming language (preferably Java/Kotlin and Python) (experience)
- Understanding of Machine Learning project lifecycle and tools (experience)
- Experience in architecting and implementing high-performance RESTful microservices (experience)
- Experience building and operating large scale distributed systems using Amazon Web Services (S3, Kinesis, Cloud Formation, EKS, AWS Security and Networking) (experience)
- Experience with leveraging LLMs effectively and optimizing model usage on GPUs (experience)
- Experience with Databricks or Apache Spark (experience)
- Experience with Continuous Delivery and Continuous Integration (experience)
Preferred Qualifications
- Expert-level SQL knowledge, query tuning, schema design, and ETL processes (experience)
- Experience with Amazon Sagemaker (experience)
- Experience with scaling and deploying Machine Learning models (experience)
Responsibilities
- Tackle the largest and most complex problems in the team, from technical design to launch
- Work closely with Product, Engineering and Design leads in Jira AI, and translate their requirements into solid engineering deliverables, delegating work to the teams
- Deliver solutions that are used by other teams and products
- Follow a Product Engineer mindset by building features that are data-driven and customer-centric, fostering that culture within the Jira AI group
- Exceptional problem solving ability using ML, AI and core software engineering
- Routinely tackle complex architecture challenges and define architectural standards
- Actively contribute to the code delivery through leading code reviews & documentation, direct contribution and fixing complex bugs in high-risk surface areas
- Expertise in data analysis, statistical methods, and logical reasoning to inform data-driven decision-making
- Partner across engineering teams to take on company-wide initiatives spanning multiple projects
- Mentor junior members on the team
Benefits
- general: Flexibility to choose where to work - in an office, from home, or a combination
- general: Ability to support family, personal goals, and other priorities
- general: Opportunity to work in any country where Atlassian has a legal entity
- general: Virtual interviews and onboarding as part of being a distributed-first company
Tags & Categories
Principal Machine Learning Engineer
Atlassian
Principal Machine Learning Engineer
Job Description
Principal Machine Learning Engineer
📋 Job Overview
The Principal Machine Learning Engineer at Atlassian will tackle complex problems, work closely with cross-functional teams, and deliver data-driven solutions within the Jira AI group. This role involves leading technical design, architecture, and code delivery, while mentoring junior team members.
📍 Location: Multiple Locations, Global
🏢 Category: Engineering
📅 Posted: 2025-12-11 03:58 PM
🎯 Key Responsibilities
- Tackle the largest and most complex problems in the team, from technical design to launch
- Work closely with Product, Engineering and Design leads in Jira AI, and translate their requirements into solid engineering deliverables, delegating work to the teams
- Deliver solutions that are used by other teams and products
- Follow a Product Engineer mindset by building features that are data-driven and customer-centric, fostering that culture within the Jira AI group
- Exceptional problem solving ability using ML, AI and core software engineering
- Routinely tackle complex architecture challenges and define architectural standards
- Actively contribute to the code delivery through leading code reviews & documentation, direct contribution and fixing complex bugs in high-risk surface areas
- Expertise in data analysis, statistical methods, and logical reasoning to inform data-driven decision-making
- Partner across engineering teams to take on company-wide initiatives spanning multiple projects
- Mentor junior members on the team
✅ Required Qualifications
- Fluency in at least one modern object-oriented programming language (preferably Java/Kotlin and Python)
- Understanding of Machine Learning project lifecycle and tools
- Experience in architecting and implementing high-performance RESTful microservices
- Experience building and operating large scale distributed systems using Amazon Web Services (S3, Kinesis, Cloud Formation, EKS, AWS Security and Networking)
- Experience with leveraging LLMs effectively and optimizing model usage on GPUs
- Experience with Databricks or Apache Spark
- Experience with Continuous Delivery and Continuous Integration
⭐ Preferred Qualifications
- Expert-level SQL knowledge, query tuning, schema design, and ETL processes
- Experience with Amazon Sagemaker
- Experience with scaling and deploying Machine Learning models
🛠️ Required Skills
- Java
- Kotlin
- Python
- Machine Learning
- RESTful microservices
- Amazon Web Services (S3, Kinesis, Cloud Formation, EKS, AWS Security and Networking)
- LLMs
- GPUs
- Databricks
- Apache Spark
- Continuous Delivery
- Continuous Integration
- Data analysis
- Statistical methods
- Logical reasoning
- Problem solving
- Architecture
- Code reviews
- Documentation
- Mentoring
🎁 Benefits & Perks
- Flexibility to choose where to work - in an office, from home, or a combination
- Ability to support family, personal goals, and other priorities
- Opportunity to work in any country where Atlassian has a legal entity
- Virtual interviews and onboarding as part of being a distributed-first company
Locations
- Multiple Locations, Global
Salary
Estimated Salary Rangemedium confidence
200,000 - 300,000 USD / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Javaintermediate
- Kotlinintermediate
- Pythonintermediate
- Machine Learningintermediate
- RESTful microservicesintermediate
- Amazon Web Services (S3, Kinesis, Cloud Formation, EKS, AWS Security and Networking)intermediate
- LLMsintermediate
- GPUsintermediate
- Databricksintermediate
- Apache Sparkintermediate
- Continuous Deliveryintermediate
- Continuous Integrationintermediate
- Data analysisintermediate
- Statistical methodsintermediate
- Logical reasoningintermediate
- Problem solvingintermediate
- Architectureintermediate
- Code reviewsintermediate
- Documentationintermediate
- Mentoringintermediate
Required Qualifications
- Fluency in at least one modern object-oriented programming language (preferably Java/Kotlin and Python) (experience)
- Understanding of Machine Learning project lifecycle and tools (experience)
- Experience in architecting and implementing high-performance RESTful microservices (experience)
- Experience building and operating large scale distributed systems using Amazon Web Services (S3, Kinesis, Cloud Formation, EKS, AWS Security and Networking) (experience)
- Experience with leveraging LLMs effectively and optimizing model usage on GPUs (experience)
- Experience with Databricks or Apache Spark (experience)
- Experience with Continuous Delivery and Continuous Integration (experience)
Preferred Qualifications
- Expert-level SQL knowledge, query tuning, schema design, and ETL processes (experience)
- Experience with Amazon Sagemaker (experience)
- Experience with scaling and deploying Machine Learning models (experience)
Responsibilities
- Tackle the largest and most complex problems in the team, from technical design to launch
- Work closely with Product, Engineering and Design leads in Jira AI, and translate their requirements into solid engineering deliverables, delegating work to the teams
- Deliver solutions that are used by other teams and products
- Follow a Product Engineer mindset by building features that are data-driven and customer-centric, fostering that culture within the Jira AI group
- Exceptional problem solving ability using ML, AI and core software engineering
- Routinely tackle complex architecture challenges and define architectural standards
- Actively contribute to the code delivery through leading code reviews & documentation, direct contribution and fixing complex bugs in high-risk surface areas
- Expertise in data analysis, statistical methods, and logical reasoning to inform data-driven decision-making
- Partner across engineering teams to take on company-wide initiatives spanning multiple projects
- Mentor junior members on the team
Benefits
- general: Flexibility to choose where to work - in an office, from home, or a combination
- general: Ability to support family, personal goals, and other priorities
- general: Opportunity to work in any country where Atlassian has a legal entity
- general: Virtual interviews and onboarding as part of being a distributed-first company
Tags & Categories