Senior Machine Learning Engineer - GenAI Modeling & Innovation Forge
Atlassian
Senior Machine Learning Engineer - GenAI Modeling & Innovation Forge
Job Description
Senior Machine Learning Engineer - GenAI Modeling & Innovation Forge
š Job Overview
Join Atlassian's GenAI Modeling & Innovation Forge in Singapore as a Senior Machine Learning Engineer. Focus on advanced GenAI modeling, rapid prototyping, and applied research to drive AI innovations across Atlassian's products. Collaborate with cross-functional teams to transition prototypes into scalable solutions.
š Location: Singapore, Singapore
š¢ Category: Engineering
š Posted: 2025-10-26 10:29 PM
šÆ Key Responsibilities
- Develop and fine-tune LLMs and embeddings for Atlassianās unique knowledge and enterprise data.
- Implement retrieval-augmented generation (RAG), hybrid retrieval, and knowledge-grounded modeling approaches.
- Work hands-on with modern frameworks, contributing directly to high-value prototypes and experiments.
- Build proof-of-concept systems for GenAI-powered assistants, agentic workflows, and innovative user experiences.
- Run experiments, collect feedback, and iterate fast to validate impact.
- Design and implement evaluation methods for quality, groundedness, and user value.
- Work closely with peers across ML, engineering, and product teams to bring new ideas to life.
- Share learnings, contribute to team best practices, and help establish the Forge as a hub of innovation.
- Support transitioning successful prototypes into scalable, production-ready solutions.
ā Required Qualifications
- Extensive experience (generally 5+ years) in ML systems engineering, backend engineering, or infrastructure roles.
- Strong background in one or more of: LLMs, NLP, search/retrieval, embeddings, or applied ML.
- Hands-on experience with at least one GenAI area: RAG pipelines, fine-tuning, hybrid retrieval, or orchestration frameworks.
- Proficiency with modern ML frameworks (PyTorch, TensorFlow, Hugging Face, LangChain, LlamaIndex).
- Familiarity with vector databases (Weaviate, Pinecone, FAISS, etc.) and large-scale serving infra.
- Strong coding skills (Python, backend engineering) and ability to move fast from idea to prototype.
- Comfort working in fast-paced, experimental environments with evolving direction.
- Bachelorās or Masterās in Computer Science, Machine Learning, or related fieldāor equivalent experience.
ā Preferred Qualifications
- Experience in multimodal models, knowledge graphs, or semantic embeddings.
- Familiarity with evaluation metrics for search/GenAI (e.g., NDCG, groundedness, hallucination detection).
- Contributions to open-source GenAI/ML projects.
- Interest in growing into technical leadership (mentorship, setting direction, cross-team influence).
š ļø Required Skills
- Machine Learning
- Backend Engineering
- Infrastructure
- LLMs
- NLP
- Search/Retrieval
- Embeddings
- Applied ML
- RAG Pipelines
- Fine-tuning
- Hybrid Retrieval
- Orchestration Frameworks
- PyTorch
- TensorFlow
- Hugging Face
- LangChain
- LlamaIndex
- Vector Databases
- Weaviate
- Pinecone
- FAISS
- Large-scale Serving Infrastructure
- Python
- Coding
- Prototyping
- Collaboration
- Experimentation
- Evaluation Methods
- Teamwork
- Innovation
- Technical Leadership
š 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
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
- Backend Engineeringintermediate
- Infrastructureintermediate
- LLMsintermediate
- NLPintermediate
- Search/Retrievalintermediate
- Embeddingsintermediate
- Applied MLintermediate
- RAG Pipelinesintermediate
- Fine-tuningintermediate
- Hybrid Retrievalintermediate
- Orchestration Frameworksintermediate
- PyTorchintermediate
- TensorFlowintermediate
- Hugging Faceintermediate
- LangChainintermediate
- LlamaIndexintermediate
- Vector Databasesintermediate
- Weaviateintermediate
- Pineconeintermediate
- FAISSintermediate
- Large-scale Serving Infrastructureintermediate
- Pythonintermediate
- Codingintermediate
- Prototypingintermediate
- Collaborationintermediate
- Experimentationintermediate
- Evaluation Methodsintermediate
- Teamworkintermediate
- Innovationintermediate
- Technical Leadershipintermediate
Required Qualifications
- Extensive experience (generally 5+ years) in ML systems engineering, backend engineering, or infrastructure roles. (experience)
- Strong background in one or more of: LLMs, NLP, search/retrieval, embeddings, or applied ML. (experience)
- Hands-on experience with at least one GenAI area: RAG pipelines, fine-tuning, hybrid retrieval, or orchestration frameworks. (experience)
- Proficiency with modern ML frameworks (PyTorch, TensorFlow, Hugging Face, LangChain, LlamaIndex). (experience)
- Familiarity with vector databases (Weaviate, Pinecone, FAISS, etc.) and large-scale serving infra. (experience)
- Strong coding skills (Python, backend engineering) and ability to move fast from idea to prototype. (experience)
- Comfort working in fast-paced, experimental environments with evolving direction. (experience)
- Bachelorās or Masterās in Computer Science, Machine Learning, or related fieldāor equivalent experience. (experience)
Preferred Qualifications
- Experience in multimodal models, knowledge graphs, or semantic embeddings. (experience)
- Familiarity with evaluation metrics for search/GenAI (e.g., NDCG, groundedness, hallucination detection). (experience)
- Contributions to open-source GenAI/ML projects. (experience)
- Interest in growing into technical leadership (mentorship, setting direction, cross-team influence). (experience)
Responsibilities
- Develop and fine-tune LLMs and embeddings for Atlassianās unique knowledge and enterprise data.
- Implement retrieval-augmented generation (RAG), hybrid retrieval, and knowledge-grounded modeling approaches.
- Work hands-on with modern frameworks, contributing directly to high-value prototypes and experiments.
- Build proof-of-concept systems for GenAI-powered assistants, agentic workflows, and innovative user experiences.
- Run experiments, collect feedback, and iterate fast to validate impact.
- Design and implement evaluation methods for quality, groundedness, and user value.
- Work closely with peers across ML, engineering, and product teams to bring new ideas to life.
- Share learnings, contribute to team best practices, and help establish the Forge as a hub of innovation.
- Support transitioning successful prototypes into scalable, production-ready solutions.
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
Senior Machine Learning Engineer - GenAI Modeling & Innovation Forge
Atlassian
Senior Machine Learning Engineer - GenAI Modeling & Innovation Forge
Job Description
Senior Machine Learning Engineer - GenAI Modeling & Innovation Forge
š Job Overview
Join Atlassian's GenAI Modeling & Innovation Forge in Singapore as a Senior Machine Learning Engineer. Focus on advanced GenAI modeling, rapid prototyping, and applied research to drive AI innovations across Atlassian's products. Collaborate with cross-functional teams to transition prototypes into scalable solutions.
š Location: Singapore, Singapore
š¢ Category: Engineering
š Posted: 2025-10-26 10:29 PM
šÆ Key Responsibilities
- Develop and fine-tune LLMs and embeddings for Atlassianās unique knowledge and enterprise data.
- Implement retrieval-augmented generation (RAG), hybrid retrieval, and knowledge-grounded modeling approaches.
- Work hands-on with modern frameworks, contributing directly to high-value prototypes and experiments.
- Build proof-of-concept systems for GenAI-powered assistants, agentic workflows, and innovative user experiences.
- Run experiments, collect feedback, and iterate fast to validate impact.
- Design and implement evaluation methods for quality, groundedness, and user value.
- Work closely with peers across ML, engineering, and product teams to bring new ideas to life.
- Share learnings, contribute to team best practices, and help establish the Forge as a hub of innovation.
- Support transitioning successful prototypes into scalable, production-ready solutions.
ā Required Qualifications
- Extensive experience (generally 5+ years) in ML systems engineering, backend engineering, or infrastructure roles.
- Strong background in one or more of: LLMs, NLP, search/retrieval, embeddings, or applied ML.
- Hands-on experience with at least one GenAI area: RAG pipelines, fine-tuning, hybrid retrieval, or orchestration frameworks.
- Proficiency with modern ML frameworks (PyTorch, TensorFlow, Hugging Face, LangChain, LlamaIndex).
- Familiarity with vector databases (Weaviate, Pinecone, FAISS, etc.) and large-scale serving infra.
- Strong coding skills (Python, backend engineering) and ability to move fast from idea to prototype.
- Comfort working in fast-paced, experimental environments with evolving direction.
- Bachelorās or Masterās in Computer Science, Machine Learning, or related fieldāor equivalent experience.
ā Preferred Qualifications
- Experience in multimodal models, knowledge graphs, or semantic embeddings.
- Familiarity with evaluation metrics for search/GenAI (e.g., NDCG, groundedness, hallucination detection).
- Contributions to open-source GenAI/ML projects.
- Interest in growing into technical leadership (mentorship, setting direction, cross-team influence).
š ļø Required Skills
- Machine Learning
- Backend Engineering
- Infrastructure
- LLMs
- NLP
- Search/Retrieval
- Embeddings
- Applied ML
- RAG Pipelines
- Fine-tuning
- Hybrid Retrieval
- Orchestration Frameworks
- PyTorch
- TensorFlow
- Hugging Face
- LangChain
- LlamaIndex
- Vector Databases
- Weaviate
- Pinecone
- FAISS
- Large-scale Serving Infrastructure
- Python
- Coding
- Prototyping
- Collaboration
- Experimentation
- Evaluation Methods
- Teamwork
- Innovation
- Technical Leadership
š 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
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
- Backend Engineeringintermediate
- Infrastructureintermediate
- LLMsintermediate
- NLPintermediate
- Search/Retrievalintermediate
- Embeddingsintermediate
- Applied MLintermediate
- RAG Pipelinesintermediate
- Fine-tuningintermediate
- Hybrid Retrievalintermediate
- Orchestration Frameworksintermediate
- PyTorchintermediate
- TensorFlowintermediate
- Hugging Faceintermediate
- LangChainintermediate
- LlamaIndexintermediate
- Vector Databasesintermediate
- Weaviateintermediate
- Pineconeintermediate
- FAISSintermediate
- Large-scale Serving Infrastructureintermediate
- Pythonintermediate
- Codingintermediate
- Prototypingintermediate
- Collaborationintermediate
- Experimentationintermediate
- Evaluation Methodsintermediate
- Teamworkintermediate
- Innovationintermediate
- Technical Leadershipintermediate
Required Qualifications
- Extensive experience (generally 5+ years) in ML systems engineering, backend engineering, or infrastructure roles. (experience)
- Strong background in one or more of: LLMs, NLP, search/retrieval, embeddings, or applied ML. (experience)
- Hands-on experience with at least one GenAI area: RAG pipelines, fine-tuning, hybrid retrieval, or orchestration frameworks. (experience)
- Proficiency with modern ML frameworks (PyTorch, TensorFlow, Hugging Face, LangChain, LlamaIndex). (experience)
- Familiarity with vector databases (Weaviate, Pinecone, FAISS, etc.) and large-scale serving infra. (experience)
- Strong coding skills (Python, backend engineering) and ability to move fast from idea to prototype. (experience)
- Comfort working in fast-paced, experimental environments with evolving direction. (experience)
- Bachelorās or Masterās in Computer Science, Machine Learning, or related fieldāor equivalent experience. (experience)
Preferred Qualifications
- Experience in multimodal models, knowledge graphs, or semantic embeddings. (experience)
- Familiarity with evaluation metrics for search/GenAI (e.g., NDCG, groundedness, hallucination detection). (experience)
- Contributions to open-source GenAI/ML projects. (experience)
- Interest in growing into technical leadership (mentorship, setting direction, cross-team influence). (experience)
Responsibilities
- Develop and fine-tune LLMs and embeddings for Atlassianās unique knowledge and enterprise data.
- Implement retrieval-augmented generation (RAG), hybrid retrieval, and knowledge-grounded modeling approaches.
- Work hands-on with modern frameworks, contributing directly to high-value prototypes and experiments.
- Build proof-of-concept systems for GenAI-powered assistants, agentic workflows, and innovative user experiences.
- Run experiments, collect feedback, and iterate fast to validate impact.
- Design and implement evaluation methods for quality, groundedness, and user value.
- Work closely with peers across ML, engineering, and product teams to bring new ideas to life.
- Share learnings, contribute to team best practices, and help establish the Forge as a hub of innovation.
- Support transitioning successful prototypes into scalable, production-ready solutions.
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