Principal Machine Learning System Engineer - Data Engineering
Atlassian
Principal Machine Learning System Engineer - Data Engineering
Job Description
Principal Machine Learning System Engineer - Data Engineering
📋 Job Overview
Lead the design, development, and deployment of scalable data and ML pipelines for search at Atlassian. Architect and optimize data infrastructure for large-scale search data, develop and productionize ML models, and drive best practices in data quality and MLOps.
📍 Location: Canada, Canada
🏢 Category: Engineering
📅 Posted: 2025-10-15 02:00 PM
🎯 Key Responsibilities
- Lead the design, development, and deployment of scalable, reliable, and high-performance data and ML pipelines for search
- Architect and optimize data infrastructure for ingesting, processing, and serving large-scale, high-velocity search and experimentation data
- Develop and productionize ML models for search ranking, relevance, and personalization
- Drive best practices in data quality, governance, reproducibility, and operational excellence for ML and data engineering workflows
- Collaborate with cross-functional teams to define data schemas, metrics, and logging standards for experimentation and search analytics
- Mentor and guide engineers across teams, raising the bar for technical excellence, experimentation rigor, and system reliability
- Champion MLOps and DataOps practices to streamline model deployment, monitoring, and lifecycle management
- Continuously improve experimentation velocity by building frameworks and tools for A/B testing, causal inference, and rapid iteration
✅ Required Qualifications
- Master or PhD in a quantitative subject (Statistics, Mathematics, Computer Science, Operations Research, or relevant work experience)
- 5+ 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)
- Experienced in building production-level data engineering pipelines
- Experience building and scaling machine learning models in business applications using large amounts of data
- Experienced in search domain
- Ability to communicate and explain data science concepts to diverse audiences
- Focus on business practicality and the 80/20 rule
- Agile development mindset
🛠️ Required Skills
- Python
- Java
- SQL
- Spark
- AWS
- Databricks
- Machine Learning
- Data Engineering
- Search
- MLOps
- DataOps
- A/B Testing
- Causal Inference
- Communication
- Agile Development
🎁 Benefits & Perks
- Health and wellbeing resources
- Paid volunteer days
- Wide range of perks and benefits designed to support you, your family and to help you engage with your local community
Locations
- Canada, Canada
Salary
188,100 - 245,575 CAD / yearly
Estimated Salary Rangemedium confidence
150,000 - 220,000 CAD / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Pythonintermediate
- Javaintermediate
- SQLintermediate
- Sparkintermediate
- AWSintermediate
- Databricksintermediate
- Machine Learningintermediate
- Data Engineeringintermediate
- Searchintermediate
- MLOpsintermediate
- DataOpsintermediate
- A/B Testingintermediate
- Causal Inferenceintermediate
- Communicationintermediate
- Agile Developmentintermediate
Required Qualifications
- Master or PhD in a quantitative subject (Statistics, Mathematics, Computer Science, Operations Research, or relevant work experience) (experience)
- 5+ years of related industry experience in the data science domain (experience)
- Expertise in Python or Java with the ability to write performant production-quality code (experience)
- Familiarity with SQL (experience)
- Knowledge of Spark and cloud data environments (e.g., AWS, Databricks) (experience)
- Experienced in building production-level data engineering pipelines (experience)
- Experience building and scaling machine learning models in business applications using large amounts of data (experience)
- Experienced in search domain (experience)
- Ability to communicate and explain data science concepts to diverse audiences (experience)
- Focus on business practicality and the 80/20 rule (experience)
- Agile development mindset (experience)
Responsibilities
- Lead the design, development, and deployment of scalable, reliable, and high-performance data and ML pipelines for search
- Architect and optimize data infrastructure for ingesting, processing, and serving large-scale, high-velocity search and experimentation data
- Develop and productionize ML models for search ranking, relevance, and personalization
- Drive best practices in data quality, governance, reproducibility, and operational excellence for ML and data engineering workflows
- Collaborate with cross-functional teams to define data schemas, metrics, and logging standards for experimentation and search analytics
- Mentor and guide engineers across teams, raising the bar for technical excellence, experimentation rigor, and system reliability
- Champion MLOps and DataOps practices to streamline model deployment, monitoring, and lifecycle management
- Continuously improve experimentation velocity by building frameworks and tools for A/B testing, causal inference, and rapid iteration
Benefits
- general: Health and wellbeing resources
- general: Paid volunteer days
- general: Wide range of perks and benefits designed to support you, your family and to help you engage with your local community
Tags & Categories
Principal Machine Learning System Engineer - Data Engineering
Atlassian
Principal Machine Learning System Engineer - Data Engineering
Job Description
Principal Machine Learning System Engineer - Data Engineering
📋 Job Overview
Lead the design, development, and deployment of scalable data and ML pipelines for search at Atlassian. Architect and optimize data infrastructure for large-scale search data, develop and productionize ML models, and drive best practices in data quality and MLOps.
📍 Location: Canada, Canada
🏢 Category: Engineering
📅 Posted: 2025-10-15 02:00 PM
🎯 Key Responsibilities
- Lead the design, development, and deployment of scalable, reliable, and high-performance data and ML pipelines for search
- Architect and optimize data infrastructure for ingesting, processing, and serving large-scale, high-velocity search and experimentation data
- Develop and productionize ML models for search ranking, relevance, and personalization
- Drive best practices in data quality, governance, reproducibility, and operational excellence for ML and data engineering workflows
- Collaborate with cross-functional teams to define data schemas, metrics, and logging standards for experimentation and search analytics
- Mentor and guide engineers across teams, raising the bar for technical excellence, experimentation rigor, and system reliability
- Champion MLOps and DataOps practices to streamline model deployment, monitoring, and lifecycle management
- Continuously improve experimentation velocity by building frameworks and tools for A/B testing, causal inference, and rapid iteration
✅ Required Qualifications
- Master or PhD in a quantitative subject (Statistics, Mathematics, Computer Science, Operations Research, or relevant work experience)
- 5+ 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)
- Experienced in building production-level data engineering pipelines
- Experience building and scaling machine learning models in business applications using large amounts of data
- Experienced in search domain
- Ability to communicate and explain data science concepts to diverse audiences
- Focus on business practicality and the 80/20 rule
- Agile development mindset
🛠️ Required Skills
- Python
- Java
- SQL
- Spark
- AWS
- Databricks
- Machine Learning
- Data Engineering
- Search
- MLOps
- DataOps
- A/B Testing
- Causal Inference
- Communication
- Agile Development
🎁 Benefits & Perks
- Health and wellbeing resources
- Paid volunteer days
- Wide range of perks and benefits designed to support you, your family and to help you engage with your local community
Locations
- Canada, Canada
Salary
188,100 - 245,575 CAD / yearly
Estimated Salary Rangemedium confidence
150,000 - 220,000 CAD / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Pythonintermediate
- Javaintermediate
- SQLintermediate
- Sparkintermediate
- AWSintermediate
- Databricksintermediate
- Machine Learningintermediate
- Data Engineeringintermediate
- Searchintermediate
- MLOpsintermediate
- DataOpsintermediate
- A/B Testingintermediate
- Causal Inferenceintermediate
- Communicationintermediate
- Agile Developmentintermediate
Required Qualifications
- Master or PhD in a quantitative subject (Statistics, Mathematics, Computer Science, Operations Research, or relevant work experience) (experience)
- 5+ years of related industry experience in the data science domain (experience)
- Expertise in Python or Java with the ability to write performant production-quality code (experience)
- Familiarity with SQL (experience)
- Knowledge of Spark and cloud data environments (e.g., AWS, Databricks) (experience)
- Experienced in building production-level data engineering pipelines (experience)
- Experience building and scaling machine learning models in business applications using large amounts of data (experience)
- Experienced in search domain (experience)
- Ability to communicate and explain data science concepts to diverse audiences (experience)
- Focus on business practicality and the 80/20 rule (experience)
- Agile development mindset (experience)
Responsibilities
- Lead the design, development, and deployment of scalable, reliable, and high-performance data and ML pipelines for search
- Architect and optimize data infrastructure for ingesting, processing, and serving large-scale, high-velocity search and experimentation data
- Develop and productionize ML models for search ranking, relevance, and personalization
- Drive best practices in data quality, governance, reproducibility, and operational excellence for ML and data engineering workflows
- Collaborate with cross-functional teams to define data schemas, metrics, and logging standards for experimentation and search analytics
- Mentor and guide engineers across teams, raising the bar for technical excellence, experimentation rigor, and system reliability
- Champion MLOps and DataOps practices to streamline model deployment, monitoring, and lifecycle management
- Continuously improve experimentation velocity by building frameworks and tools for A/B testing, causal inference, and rapid iteration
Benefits
- general: Health and wellbeing resources
- general: Paid volunteer days
- general: Wide range of perks and benefits designed to support you, your family and to help you engage with your local community
Tags & Categories