Senior Machine Learning System Engineer
Atlassian
Senior Machine Learning System Engineer
Job Description
Senior Machine Learning System Engineer
📋 Job Overview
As a Senior ML System Engineer at Atlassian, you will develop and refine the core infrastructure that enables the creation, training, evaluation, deployment, and management of 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: 2025-10-21 04:20 AM
🎯 Key Responsibilities
- Develop and refine core infrastructure for creating, training, evaluating, deploying, and managing Machine Learning models and pipelines
- Collaborate with product teams like Jira and Confluence to solve their specific challenges in building ML solutions
- Lead projects from technical design to launch, partnering with various teams and internal stakeholders
- Collaborate with teammates to solve complex problems
- Deliver cutting-edge solutions used by other Atlassian teams and products to build AI features
- Deliver code reviews, documentation & bug fixes within a strong engineering culture
- Partner across engineering teams on company-wide initiatives
- Mentor junior members of the team
✅ Required Qualifications
- 5+ years of experience in building Machine Learning and AI infrastructure/platform/system
- 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
- MLOps and automation: Deep experience implementing MLOps, CI/CD pipelines, and automation for continuous training, deployment, and monitoring of ML models
⭐ Preferred Qualifications
- Expert-level proficiency in Python and ML frameworks like PyTorch, TensorFlow, or JAX
- Familiarity with other languages like Go, Java, or Scala
- 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 with distributed computing frameworks for large-scale data processing, such as Spark, Ray, or Dask
- Demonstrated ability to diagnose and solve complex performance and optimization problems for ML models and infrastructure
- Experience with GenAI frameworks and tools, including developing and fine-tuning large language models (LLMs) and building retrieval-augmented generation (RAG) systems
🛠️ Required Skills
- Machine Learning
- Artificial Intelligence
- ML lifecycle
- Large-scale system design
- Distributed systems
- MLOps
- CI/CD pipelines
- Automation
- Python
- PyTorch
- TensorFlow
- JAX
- Go
- Java
- Scala
- AWS
- GCP
- Azure
- GPUs
- Spark
- Ray
- Dask
- Performance optimization
- Generative AI
- Large Language Models (LLMs)
- Retrieval-augmented generation (RAG)
- Collaboration
- Leadership
- Mentoring
- Problem-solving
- Technical design
🎁 Benefits & Perks
- Health and wellbeing resources
- Paid volunteer days
- Wide range of perks and benefits to support employees, their families, and community engagement
Locations
- Singapore, Singapore
Salary
Estimated Salary Rangemedium confidence
150,000 - 220,000 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
- Artificial Intelligenceintermediate
- ML lifecycleintermediate
- Large-scale system designintermediate
- Distributed systemsintermediate
- MLOpsintermediate
- CI/CD pipelinesintermediate
- Automationintermediate
- Pythonintermediate
- PyTorchintermediate
- TensorFlowintermediate
- JAXintermediate
- Gointermediate
- Javaintermediate
- Scalaintermediate
- AWSintermediate
- GCPintermediate
- Azureintermediate
- GPUsintermediate
- Sparkintermediate
- Rayintermediate
- Daskintermediate
- Performance optimizationintermediate
- Generative AIintermediate
- Large Language Models (LLMs)intermediate
- Retrieval-augmented generation (RAG)intermediate
- Collaborationintermediate
- Leadershipintermediate
- Mentoringintermediate
- Problem-solvingintermediate
- Technical designintermediate
Required Qualifications
- 5+ years of experience in building Machine Learning and AI infrastructure/platform/system (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)
- MLOps and automation: Deep experience implementing MLOps, CI/CD pipelines, and automation for continuous training, deployment, and monitoring of ML models (experience)
Preferred Qualifications
- Expert-level proficiency in Python and ML frameworks like PyTorch, TensorFlow, or JAX (experience)
- Familiarity with other languages like Go, Java, or Scala (experience)
- 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)
- Experience with distributed computing frameworks for large-scale data processing, such as Spark, Ray, or Dask (experience)
- Demonstrated ability to diagnose and solve complex performance and optimization problems for ML models and infrastructure (experience)
- Experience with GenAI frameworks and tools, including developing and fine-tuning large language models (LLMs) and building retrieval-augmented generation (RAG) systems (experience)
Responsibilities
- Develop and refine core infrastructure for creating, training, evaluating, deploying, and managing Machine Learning models and pipelines
- Collaborate with product teams like Jira and Confluence to solve their specific challenges in building ML solutions
- Lead projects from technical design to launch, partnering with various teams and internal stakeholders
- Collaborate with teammates to solve complex problems
- Deliver cutting-edge solutions used by other Atlassian teams and products to build AI features
- Deliver code reviews, documentation & bug fixes within a strong engineering culture
- Partner across engineering teams on company-wide initiatives
- Mentor junior members of the team
Benefits
- general: Health and wellbeing resources
- general: Paid volunteer days
- general: Wide range of perks and benefits to support employees, their families, and community engagement
Tags & Categories
Senior Machine Learning System Engineer
Atlassian
Senior Machine Learning System Engineer
Job Description
Senior Machine Learning System Engineer
📋 Job Overview
As a Senior ML System Engineer at Atlassian, you will develop and refine the core infrastructure that enables the creation, training, evaluation, deployment, and management of 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: 2025-10-21 04:20 AM
🎯 Key Responsibilities
- Develop and refine core infrastructure for creating, training, evaluating, deploying, and managing Machine Learning models and pipelines
- Collaborate with product teams like Jira and Confluence to solve their specific challenges in building ML solutions
- Lead projects from technical design to launch, partnering with various teams and internal stakeholders
- Collaborate with teammates to solve complex problems
- Deliver cutting-edge solutions used by other Atlassian teams and products to build AI features
- Deliver code reviews, documentation & bug fixes within a strong engineering culture
- Partner across engineering teams on company-wide initiatives
- Mentor junior members of the team
✅ Required Qualifications
- 5+ years of experience in building Machine Learning and AI infrastructure/platform/system
- 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
- MLOps and automation: Deep experience implementing MLOps, CI/CD pipelines, and automation for continuous training, deployment, and monitoring of ML models
⭐ Preferred Qualifications
- Expert-level proficiency in Python and ML frameworks like PyTorch, TensorFlow, or JAX
- Familiarity with other languages like Go, Java, or Scala
- 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 with distributed computing frameworks for large-scale data processing, such as Spark, Ray, or Dask
- Demonstrated ability to diagnose and solve complex performance and optimization problems for ML models and infrastructure
- Experience with GenAI frameworks and tools, including developing and fine-tuning large language models (LLMs) and building retrieval-augmented generation (RAG) systems
🛠️ Required Skills
- Machine Learning
- Artificial Intelligence
- ML lifecycle
- Large-scale system design
- Distributed systems
- MLOps
- CI/CD pipelines
- Automation
- Python
- PyTorch
- TensorFlow
- JAX
- Go
- Java
- Scala
- AWS
- GCP
- Azure
- GPUs
- Spark
- Ray
- Dask
- Performance optimization
- Generative AI
- Large Language Models (LLMs)
- Retrieval-augmented generation (RAG)
- Collaboration
- Leadership
- Mentoring
- Problem-solving
- Technical design
🎁 Benefits & Perks
- Health and wellbeing resources
- Paid volunteer days
- Wide range of perks and benefits to support employees, their families, and community engagement
Locations
- Singapore, Singapore
Salary
Estimated Salary Rangemedium confidence
150,000 - 220,000 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
- Artificial Intelligenceintermediate
- ML lifecycleintermediate
- Large-scale system designintermediate
- Distributed systemsintermediate
- MLOpsintermediate
- CI/CD pipelinesintermediate
- Automationintermediate
- Pythonintermediate
- PyTorchintermediate
- TensorFlowintermediate
- JAXintermediate
- Gointermediate
- Javaintermediate
- Scalaintermediate
- AWSintermediate
- GCPintermediate
- Azureintermediate
- GPUsintermediate
- Sparkintermediate
- Rayintermediate
- Daskintermediate
- Performance optimizationintermediate
- Generative AIintermediate
- Large Language Models (LLMs)intermediate
- Retrieval-augmented generation (RAG)intermediate
- Collaborationintermediate
- Leadershipintermediate
- Mentoringintermediate
- Problem-solvingintermediate
- Technical designintermediate
Required Qualifications
- 5+ years of experience in building Machine Learning and AI infrastructure/platform/system (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)
- MLOps and automation: Deep experience implementing MLOps, CI/CD pipelines, and automation for continuous training, deployment, and monitoring of ML models (experience)
Preferred Qualifications
- Expert-level proficiency in Python and ML frameworks like PyTorch, TensorFlow, or JAX (experience)
- Familiarity with other languages like Go, Java, or Scala (experience)
- 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)
- Experience with distributed computing frameworks for large-scale data processing, such as Spark, Ray, or Dask (experience)
- Demonstrated ability to diagnose and solve complex performance and optimization problems for ML models and infrastructure (experience)
- Experience with GenAI frameworks and tools, including developing and fine-tuning large language models (LLMs) and building retrieval-augmented generation (RAG) systems (experience)
Responsibilities
- Develop and refine core infrastructure for creating, training, evaluating, deploying, and managing Machine Learning models and pipelines
- Collaborate with product teams like Jira and Confluence to solve their specific challenges in building ML solutions
- Lead projects from technical design to launch, partnering with various teams and internal stakeholders
- Collaborate with teammates to solve complex problems
- Deliver cutting-edge solutions used by other Atlassian teams and products to build AI features
- Deliver code reviews, documentation & bug fixes within a strong engineering culture
- Partner across engineering teams on company-wide initiatives
- Mentor junior members of the team
Benefits
- general: Health and wellbeing resources
- general: Paid volunteer days
- general: Wide range of perks and benefits to support employees, their families, and community engagement
Tags & Categories