Senior Machine Learning Engineering Manager, Gen AI
Atlassian
Senior Machine Learning Engineering Manager, Gen AI
Job Description
Senior Machine Learning Engineering Manager, Gen AI
š Job Overview
Lead the development of end-to-end GenAI products at Atlassian, focusing on LLM-powered AI solutions. Drive innovation by integrating advanced AI modeling, RAG architectures, and user-centric design. Manage and grow a cross-functional team to deliver high-value user experiences across Atlassian's product suite.
š Location: Seattle, United States
š¢ Category: Engineering
š Posted: 2025-12-11 06:20 PM
šÆ Key Responsibilities
- Lead the vision, design, and execution of LLM-powered AI products
- Define system architecture across retrievers, rankers, orchestration layers, prompt templates, and feedback mechanisms
- Work closely with product and design teams to ensure delightful, fast, and grounded user experiences
- Build and manage a cross-disciplinary team including ML engineers, backend/frontend engineers, and applied scientists
- Foster a culture of E2E ownership ā empowering the team to move from prototype to production quickly and iteratively
- Mentor individuals to grow in both technical depth and product acumen
- Shape the technical roadmap and long-term strategy for GenAI search across Atlassianās product suite
- Partner with platform and infra teams to scale inference, evaluate performance, and integrate usage signals for continuous improvement
- Champion data quality, grounding, and responsible AI practices in all deployed features
ā Required Qualifications
- 8+ years in ML, search, or backend engineering roles
- 3+ years leading teams
- Strong track record of shipping ML-powered or LLM-integrated user-facing products
- Experience with RAG systems (vector search, hybrid retrieval, LLM orchestration)
- Deep experience in either modeling (e.g., LLMs, search, NLP) or engineering (e.g., backend infra, full-stack)
- Deep understanding of LLM ecosystems (OpenAI, Claude, Mistral, OSS), orchestration frameworks (LangChain, LlamaIndex), and vector databases (Weaviate, Pinecone, FAISS, etc.)
- Strong product intuition and ability to translate complex tech into valuable user features
- Familiarity with GenAI evaluation methods: hallucination detection, groundedness scoring, and human-in-the-loop feedback loops
- Masterās or PhD in Computer Science, Machine Learning, or related field preferredāor equivalent practical experience
ā Preferred Qualifications
- Experience with front-end or full-stack development for GenAI interfaces
- Familiarity with knowledge graphs, semantic embeddings, or search evaluation metrics (e.g., NDCG, precision@k)
- Passion for AI safety, ethics, and user trust in generative systems
š ļø Required Skills
- Machine Learning
- Search
- Backend Engineering
- Leadership
- RAG systems
- Vector search
- Hybrid retrieval
- LLM orchestration
- Modeling
- LLMs
- NLP
- Engineering
- Backend infrastructure
- Full-stack development
- LLM ecosystems
- OpenAI
- Claude
- Mistral
- OSS
- Orchestration frameworks
- LangChain
- LlamaIndex
- Vector databases
- Weaviate
- Pinecone
- FAISS
- Product intuition
- GenAI evaluation methods
- Hallucination detection
- Groundedness scoring
- Human-in-the-loop feedback loops
- Mentoring
- Technical roadmap
- Strategy
- Data quality
- Responsible AI practices
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
- Seattle, United States
Salary
193,500 - 303,150 USD / yearly
Estimated Salary Rangemedium confidence
250,000 - 350,000 USD / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Machine Learningintermediate
- Searchintermediate
- Backend Engineeringintermediate
- Leadershipintermediate
- RAG systemsintermediate
- Vector searchintermediate
- Hybrid retrievalintermediate
- LLM orchestrationintermediate
- Modelingintermediate
- LLMsintermediate
- NLPintermediate
- Engineeringintermediate
- Backend infrastructureintermediate
- Full-stack developmentintermediate
- LLM ecosystemsintermediate
- OpenAIintermediate
- Claudeintermediate
- Mistralintermediate
- OSSintermediate
- Orchestration frameworksintermediate
- LangChainintermediate
- LlamaIndexintermediate
- Vector databasesintermediate
- Weaviateintermediate
- Pineconeintermediate
- FAISSintermediate
- Product intuitionintermediate
- GenAI evaluation methodsintermediate
- Hallucination detectionintermediate
- Groundedness scoringintermediate
- Human-in-the-loop feedback loopsintermediate
- Mentoringintermediate
- Technical roadmapintermediate
- Strategyintermediate
- Data qualityintermediate
- Responsible AI practicesintermediate
Required Qualifications
- 8+ years in ML, search, or backend engineering roles (experience)
- 3+ years leading teams (experience)
- Strong track record of shipping ML-powered or LLM-integrated user-facing products (experience)
- Experience with RAG systems (vector search, hybrid retrieval, LLM orchestration) (experience)
- Deep experience in either modeling (e.g., LLMs, search, NLP) or engineering (e.g., backend infra, full-stack) (experience)
- Deep understanding of LLM ecosystems (OpenAI, Claude, Mistral, OSS), orchestration frameworks (LangChain, LlamaIndex), and vector databases (Weaviate, Pinecone, FAISS, etc.) (experience)
- Strong product intuition and ability to translate complex tech into valuable user features (experience)
- Familiarity with GenAI evaluation methods: hallucination detection, groundedness scoring, and human-in-the-loop feedback loops (experience)
- Masterās or PhD in Computer Science, Machine Learning, or related field preferredāor equivalent practical experience (experience)
Preferred Qualifications
- Experience with front-end or full-stack development for GenAI interfaces (experience)
- Familiarity with knowledge graphs, semantic embeddings, or search evaluation metrics (e.g., NDCG, precision@k) (experience)
- Passion for AI safety, ethics, and user trust in generative systems (experience)
Responsibilities
- Lead the vision, design, and execution of LLM-powered AI products
- Define system architecture across retrievers, rankers, orchestration layers, prompt templates, and feedback mechanisms
- Work closely with product and design teams to ensure delightful, fast, and grounded user experiences
- Build and manage a cross-disciplinary team including ML engineers, backend/frontend engineers, and applied scientists
- Foster a culture of E2E ownership ā empowering the team to move from prototype to production quickly and iteratively
- Mentor individuals to grow in both technical depth and product acumen
- Shape the technical roadmap and long-term strategy for GenAI search across Atlassianās product suite
- Partner with platform and infra teams to scale inference, evaluate performance, and integrate usage signals for continuous improvement
- Champion data quality, grounding, and responsible AI practices in all deployed features
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 Engineering Manager, Gen AI
Atlassian
Senior Machine Learning Engineering Manager, Gen AI
Job Description
Senior Machine Learning Engineering Manager, Gen AI
š Job Overview
Lead the development of end-to-end GenAI products at Atlassian, focusing on LLM-powered AI solutions. Drive innovation by integrating advanced AI modeling, RAG architectures, and user-centric design. Manage and grow a cross-functional team to deliver high-value user experiences across Atlassian's product suite.
š Location: Seattle, United States
š¢ Category: Engineering
š Posted: 2025-12-11 06:20 PM
šÆ Key Responsibilities
- Lead the vision, design, and execution of LLM-powered AI products
- Define system architecture across retrievers, rankers, orchestration layers, prompt templates, and feedback mechanisms
- Work closely with product and design teams to ensure delightful, fast, and grounded user experiences
- Build and manage a cross-disciplinary team including ML engineers, backend/frontend engineers, and applied scientists
- Foster a culture of E2E ownership ā empowering the team to move from prototype to production quickly and iteratively
- Mentor individuals to grow in both technical depth and product acumen
- Shape the technical roadmap and long-term strategy for GenAI search across Atlassianās product suite
- Partner with platform and infra teams to scale inference, evaluate performance, and integrate usage signals for continuous improvement
- Champion data quality, grounding, and responsible AI practices in all deployed features
ā Required Qualifications
- 8+ years in ML, search, or backend engineering roles
- 3+ years leading teams
- Strong track record of shipping ML-powered or LLM-integrated user-facing products
- Experience with RAG systems (vector search, hybrid retrieval, LLM orchestration)
- Deep experience in either modeling (e.g., LLMs, search, NLP) or engineering (e.g., backend infra, full-stack)
- Deep understanding of LLM ecosystems (OpenAI, Claude, Mistral, OSS), orchestration frameworks (LangChain, LlamaIndex), and vector databases (Weaviate, Pinecone, FAISS, etc.)
- Strong product intuition and ability to translate complex tech into valuable user features
- Familiarity with GenAI evaluation methods: hallucination detection, groundedness scoring, and human-in-the-loop feedback loops
- Masterās or PhD in Computer Science, Machine Learning, or related field preferredāor equivalent practical experience
ā Preferred Qualifications
- Experience with front-end or full-stack development for GenAI interfaces
- Familiarity with knowledge graphs, semantic embeddings, or search evaluation metrics (e.g., NDCG, precision@k)
- Passion for AI safety, ethics, and user trust in generative systems
š ļø Required Skills
- Machine Learning
- Search
- Backend Engineering
- Leadership
- RAG systems
- Vector search
- Hybrid retrieval
- LLM orchestration
- Modeling
- LLMs
- NLP
- Engineering
- Backend infrastructure
- Full-stack development
- LLM ecosystems
- OpenAI
- Claude
- Mistral
- OSS
- Orchestration frameworks
- LangChain
- LlamaIndex
- Vector databases
- Weaviate
- Pinecone
- FAISS
- Product intuition
- GenAI evaluation methods
- Hallucination detection
- Groundedness scoring
- Human-in-the-loop feedback loops
- Mentoring
- Technical roadmap
- Strategy
- Data quality
- Responsible AI practices
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
- Seattle, United States
Salary
193,500 - 303,150 USD / yearly
Estimated Salary Rangemedium confidence
250,000 - 350,000 USD / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Machine Learningintermediate
- Searchintermediate
- Backend Engineeringintermediate
- Leadershipintermediate
- RAG systemsintermediate
- Vector searchintermediate
- Hybrid retrievalintermediate
- LLM orchestrationintermediate
- Modelingintermediate
- LLMsintermediate
- NLPintermediate
- Engineeringintermediate
- Backend infrastructureintermediate
- Full-stack developmentintermediate
- LLM ecosystemsintermediate
- OpenAIintermediate
- Claudeintermediate
- Mistralintermediate
- OSSintermediate
- Orchestration frameworksintermediate
- LangChainintermediate
- LlamaIndexintermediate
- Vector databasesintermediate
- Weaviateintermediate
- Pineconeintermediate
- FAISSintermediate
- Product intuitionintermediate
- GenAI evaluation methodsintermediate
- Hallucination detectionintermediate
- Groundedness scoringintermediate
- Human-in-the-loop feedback loopsintermediate
- Mentoringintermediate
- Technical roadmapintermediate
- Strategyintermediate
- Data qualityintermediate
- Responsible AI practicesintermediate
Required Qualifications
- 8+ years in ML, search, or backend engineering roles (experience)
- 3+ years leading teams (experience)
- Strong track record of shipping ML-powered or LLM-integrated user-facing products (experience)
- Experience with RAG systems (vector search, hybrid retrieval, LLM orchestration) (experience)
- Deep experience in either modeling (e.g., LLMs, search, NLP) or engineering (e.g., backend infra, full-stack) (experience)
- Deep understanding of LLM ecosystems (OpenAI, Claude, Mistral, OSS), orchestration frameworks (LangChain, LlamaIndex), and vector databases (Weaviate, Pinecone, FAISS, etc.) (experience)
- Strong product intuition and ability to translate complex tech into valuable user features (experience)
- Familiarity with GenAI evaluation methods: hallucination detection, groundedness scoring, and human-in-the-loop feedback loops (experience)
- Masterās or PhD in Computer Science, Machine Learning, or related field preferredāor equivalent practical experience (experience)
Preferred Qualifications
- Experience with front-end or full-stack development for GenAI interfaces (experience)
- Familiarity with knowledge graphs, semantic embeddings, or search evaluation metrics (e.g., NDCG, precision@k) (experience)
- Passion for AI safety, ethics, and user trust in generative systems (experience)
Responsibilities
- Lead the vision, design, and execution of LLM-powered AI products
- Define system architecture across retrievers, rankers, orchestration layers, prompt templates, and feedback mechanisms
- Work closely with product and design teams to ensure delightful, fast, and grounded user experiences
- Build and manage a cross-disciplinary team including ML engineers, backend/frontend engineers, and applied scientists
- Foster a culture of E2E ownership ā empowering the team to move from prototype to production quickly and iteratively
- Mentor individuals to grow in both technical depth and product acumen
- Shape the technical roadmap and long-term strategy for GenAI search across Atlassianās product suite
- Partner with platform and infra teams to scale inference, evaluate performance, and integrate usage signals for continuous improvement
- Champion data quality, grounding, and responsible AI practices in all deployed features
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