Principal Machine Learning System Engineer
Atlassian
Principal Machine Learning System Engineer
Job Description
Principal Machine Learning System Engineer
📋 Job Overview
As a Principal Machine Learning System Engineer at Atlassian, you will develop and refine core infrastructure to empower software engineers, ML engineers, and data scientists in creating, training, evaluating, deploying, and managing Machine Learning models and pipelines. You will collaborate with product teams like Jira and Confluence to address their specific ML challenges and lead projects from technical design to launch, impacting millions of customers.
📍 Location: Singapore, Singapore
🏢 Category: Engineering
📅 Posted: 2025-12-08 03:48 AM
🎯 Key Responsibilities
- Collaborate with teammates to solve complex problems, from technical design to launch
- Deliver cutting-edge solutions used by other Atlassian teams and products to build AI features that reach millions of customers
- Deliver code reviews, documentation & bug fixes within a strong engineering culture
- Partner across engineering teams to take on company-wide initiatives spanning multiple projects
- Mentor junior members of the team
✅ Required Qualifications
- Extensive experience in building Machine Learning and AI infra/platform/system (generally 5+ years)
- Comprehensive ML lifecycle expertise: proven experience developing, deploying, and maintaining end-to-end ML systems, from data engineering to model serving and monitoring
- Large-scale system design: Extensive experience designing and building scalable, fault-tolerant, and high-performance distributed systems for machine learning
- Proficiency with frameworks and languages: Expert-level proficiency in Python and ML frameworks like PyTorch, TensorFlow, or JAX
- MLOps and automation: Deep experience implementing MLOps, CI/CD pipelines, and automation for continuous training, deployment, and monitoring of ML models
⭐ Preferred Qualifications
- Cloud infrastructure: Hands-on expertise with major cloud platforms such as AWS, GCP, or Azure, including their specific AI/ML services and compute resources like GPUs
- Big data processing: Experience with distributed computing frameworks for large-scale data processing, such as Spark, Ray, or Dask
- Performance optimization: A demonstrated ability to diagnose and solve complex performance and optimization problems for ML models and infrastructure
- Generative AI systems: Experience with GenAI frameworks and tools, including developing and fine-tuning large language models (LLMs) and building retrieval-augmented generation (RAG) systems
- Familiarity with other languages like Go, Java, or Scala
🛠️ Required Skills
- Machine Learning
- AI infrastructure
- ML lifecycle
- Large-scale system design
- Python
- PyTorch
- TensorFlow
- JAX
- MLOps
- CI/CD pipelines
- Automation
- Collaboration
- Technical design
- Code reviews
- Documentation
- Mentoring
- Go
- Java
- Scala
- AWS
- GCP
- Azure
- GPUs
- Spark
- Ray
- Dask
- Performance optimization
- Generative AI
- Large Language Models (LLMs)
- Retrieval-augmented generation (RAG)
🎁 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
- Singapore, Singapore
Salary
Estimated Salary Rangemedium confidence
180,000 - 250,000 SGD / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Machine Learningintermediate
- AI infrastructureintermediate
- ML lifecycleintermediate
- Large-scale system designintermediate
- Pythonintermediate
- PyTorchintermediate
- TensorFlowintermediate
- JAXintermediate
- MLOpsintermediate
- CI/CD pipelinesintermediate
- Automationintermediate
- Collaborationintermediate
- Technical designintermediate
- Code reviewsintermediate
- Documentationintermediate
- Mentoringintermediate
- Gointermediate
- Javaintermediate
- Scalaintermediate
- AWSintermediate
- GCPintermediate
- Azureintermediate
- GPUsintermediate
- Sparkintermediate
- Rayintermediate
- Daskintermediate
- Performance optimizationintermediate
- Generative AIintermediate
- Large Language Models (LLMs)intermediate
- Retrieval-augmented generation (RAG)intermediate
Required Qualifications
- Extensive experience in building Machine Learning and AI infra/platform/system (generally 5+ years) (experience)
- Comprehensive ML lifecycle expertise: proven experience developing, deploying, and maintaining end-to-end ML systems, from data engineering to model serving and monitoring (experience)
- Large-scale system design: Extensive experience designing and building scalable, fault-tolerant, and high-performance distributed systems for machine learning (experience)
- Proficiency with frameworks and languages: Expert-level proficiency in Python and ML frameworks like PyTorch, TensorFlow, or JAX (experience)
- MLOps and automation: Deep experience implementing MLOps, CI/CD pipelines, and automation for continuous training, deployment, and monitoring of ML models (experience)
Preferred Qualifications
- Cloud infrastructure: Hands-on expertise with major cloud platforms such as AWS, GCP, or Azure, including their specific AI/ML services and compute resources like GPUs (experience)
- Big data processing: Experience with distributed computing frameworks for large-scale data processing, such as Spark, Ray, or Dask (experience)
- Performance optimization: A demonstrated ability to diagnose and solve complex performance and optimization problems for ML models and infrastructure (experience)
- Generative AI systems: Experience with GenAI frameworks and tools, including developing and fine-tuning large language models (LLMs) and building retrieval-augmented generation (RAG) systems (experience)
- Familiarity with other languages like Go, Java, or Scala (experience)
Responsibilities
- Collaborate with teammates to solve complex problems, from technical design to launch
- Deliver cutting-edge solutions used by other Atlassian teams and products to build AI features that reach millions of customers
- Deliver code reviews, documentation & bug fixes within a strong engineering culture
- Partner across engineering teams to take on company-wide initiatives spanning multiple projects
- Mentor junior members of the team
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
Atlassian
Principal Machine Learning System Engineer
Job Description
Principal Machine Learning System Engineer
📋 Job Overview
As a Principal Machine Learning System Engineer at Atlassian, you will develop and refine core infrastructure to empower software engineers, ML engineers, and data scientists in creating, training, evaluating, deploying, and managing Machine Learning models and pipelines. You will collaborate with product teams like Jira and Confluence to address their specific ML challenges and lead projects from technical design to launch, impacting millions of customers.
📍 Location: Singapore, Singapore
🏢 Category: Engineering
📅 Posted: 2025-12-08 03:48 AM
🎯 Key Responsibilities
- Collaborate with teammates to solve complex problems, from technical design to launch
- Deliver cutting-edge solutions used by other Atlassian teams and products to build AI features that reach millions of customers
- Deliver code reviews, documentation & bug fixes within a strong engineering culture
- Partner across engineering teams to take on company-wide initiatives spanning multiple projects
- Mentor junior members of the team
✅ Required Qualifications
- Extensive experience in building Machine Learning and AI infra/platform/system (generally 5+ years)
- Comprehensive ML lifecycle expertise: proven experience developing, deploying, and maintaining end-to-end ML systems, from data engineering to model serving and monitoring
- Large-scale system design: Extensive experience designing and building scalable, fault-tolerant, and high-performance distributed systems for machine learning
- Proficiency with frameworks and languages: Expert-level proficiency in Python and ML frameworks like PyTorch, TensorFlow, or JAX
- MLOps and automation: Deep experience implementing MLOps, CI/CD pipelines, and automation for continuous training, deployment, and monitoring of ML models
⭐ Preferred Qualifications
- Cloud infrastructure: Hands-on expertise with major cloud platforms such as AWS, GCP, or Azure, including their specific AI/ML services and compute resources like GPUs
- Big data processing: Experience with distributed computing frameworks for large-scale data processing, such as Spark, Ray, or Dask
- Performance optimization: A demonstrated ability to diagnose and solve complex performance and optimization problems for ML models and infrastructure
- Generative AI systems: Experience with GenAI frameworks and tools, including developing and fine-tuning large language models (LLMs) and building retrieval-augmented generation (RAG) systems
- Familiarity with other languages like Go, Java, or Scala
🛠️ Required Skills
- Machine Learning
- AI infrastructure
- ML lifecycle
- Large-scale system design
- Python
- PyTorch
- TensorFlow
- JAX
- MLOps
- CI/CD pipelines
- Automation
- Collaboration
- Technical design
- Code reviews
- Documentation
- Mentoring
- Go
- Java
- Scala
- AWS
- GCP
- Azure
- GPUs
- Spark
- Ray
- Dask
- Performance optimization
- Generative AI
- Large Language Models (LLMs)
- Retrieval-augmented generation (RAG)
🎁 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
- Singapore, Singapore
Salary
Estimated Salary Rangemedium confidence
180,000 - 250,000 SGD / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Machine Learningintermediate
- AI infrastructureintermediate
- ML lifecycleintermediate
- Large-scale system designintermediate
- Pythonintermediate
- PyTorchintermediate
- TensorFlowintermediate
- JAXintermediate
- MLOpsintermediate
- CI/CD pipelinesintermediate
- Automationintermediate
- Collaborationintermediate
- Technical designintermediate
- Code reviewsintermediate
- Documentationintermediate
- Mentoringintermediate
- Gointermediate
- Javaintermediate
- Scalaintermediate
- AWSintermediate
- GCPintermediate
- Azureintermediate
- GPUsintermediate
- Sparkintermediate
- Rayintermediate
- Daskintermediate
- Performance optimizationintermediate
- Generative AIintermediate
- Large Language Models (LLMs)intermediate
- Retrieval-augmented generation (RAG)intermediate
Required Qualifications
- Extensive experience in building Machine Learning and AI infra/platform/system (generally 5+ years) (experience)
- Comprehensive ML lifecycle expertise: proven experience developing, deploying, and maintaining end-to-end ML systems, from data engineering to model serving and monitoring (experience)
- Large-scale system design: Extensive experience designing and building scalable, fault-tolerant, and high-performance distributed systems for machine learning (experience)
- Proficiency with frameworks and languages: Expert-level proficiency in Python and ML frameworks like PyTorch, TensorFlow, or JAX (experience)
- MLOps and automation: Deep experience implementing MLOps, CI/CD pipelines, and automation for continuous training, deployment, and monitoring of ML models (experience)
Preferred Qualifications
- Cloud infrastructure: Hands-on expertise with major cloud platforms such as AWS, GCP, or Azure, including their specific AI/ML services and compute resources like GPUs (experience)
- Big data processing: Experience with distributed computing frameworks for large-scale data processing, such as Spark, Ray, or Dask (experience)
- Performance optimization: A demonstrated ability to diagnose and solve complex performance and optimization problems for ML models and infrastructure (experience)
- Generative AI systems: Experience with GenAI frameworks and tools, including developing and fine-tuning large language models (LLMs) and building retrieval-augmented generation (RAG) systems (experience)
- Familiarity with other languages like Go, Java, or Scala (experience)
Responsibilities
- Collaborate with teammates to solve complex problems, from technical design to launch
- Deliver cutting-edge solutions used by other Atlassian teams and products to build AI features that reach millions of customers
- Deliver code reviews, documentation & bug fixes within a strong engineering culture
- Partner across engineering teams to take on company-wide initiatives spanning multiple projects
- Mentor junior members of the team
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