Job description

At Atlassian, we’re on a mission to unleash the potential of every team. As part of that mission, we're investing deeply in Generative AI — pioneering advanced modeling and rapid innovations that accelerate how teams work, discover, and create. We’re seeking a Principal Machine Learning Systems Engineer (P60) to lead technical directions of GenAI Products & Knowledge Innovations in US. You’ll focus on building and scaling the systems that power advanced GenAI products with enterprise knowledge, rapid prototyping, and applied research—bridging cutting-edge algorithms with reliable, high-performance infrastructure. Working at Atlassian Atlassians can choose where they work – whether in an office, from home, or a combination of the two. That way, Atlassians have more control over supporting their family, personal goals, and other priorities. We can hire people in any country where we have a legal entity. What You'll Do 🛠 Design and Build ML Systems • Architect and implement scalable systems for training, fine-tuning, and serving large language models and embeddings. • Build efficient retrieval, hybrid search, and RAG pipelines integrated with knowledge-grounded data. • Develop tools and infra to support rapid experimentation, evaluation, and deployment of prototypes. ⚡ Enable Rapid Prototyping & Applied Research • Partner with applied scientists to bring new ideas to life in robust, production-ready pipelines. • Build proof-of-concept (POC) systems and evolve them into reliable, scalable services. • Optimize latency, throughput, and resource efficiency for GenAI workloads. 🤝 Collaborate Across Disciplines • Work closely with ML engineers, backend developers, and product teams to ship end-to-end innovations. • Contribute to best practices in model deployment, monitoring, and evaluation. • Help establish the team as a world-class hub for GenAI systems innovation. What We're Looking For ✅ Experience • 6+ years in ML systems engineering, backend engineering, or infrastructure roles. • Strong track record of building and scaling ML-powered services in production. • Experience with large-scale model training, inference pipelines, or search/retrieval systems. 💡 Skills • Proficiency in backend systems and ML frameworks (Python, PyTorch, TensorFlow, Hugging Face). • Experience with vector databases (Weaviate, Pinecone, FAISS), orchestration frameworks (LangChain, LlamaIndex). • Strong coding skills and ability to optimize systems for performance and reliability. • Familiarity with cloud environments (AWS, GCP, Azure) and container/orchestration tools (Kubernetes, Docker). 📚 Education • Bachelor’s or Master’s in Computer Science, Machine Learning, or related field—or equivalent industry experience. Nice to Have • Background in distributed systems, high-performance computing, or GPU optimization. • Familiarity with search/GenAI evaluation metrics (e.g., NDCG, groundedness, latency benchmarks). • Experience with monitoring, observability, and reliability practices for ML systems. • Contributions to open-source infra or ML systems frameworks. Benefits & Perks Atlassian offers a wide range of perks and benefits designed to support you, your family and to help you engage with your local community. Our offerings include health and wellbeing resources, paid volunteer days, and so much more. To learn more, visit go.atlassian.com/perksandbenefits . About Atlassian At Atlassian, we're motivated by a common goal: to unleash the potential of every team. Our software products help teams all over the planet and our solutions are designed for all types of work. Team collaboration through our tools makes what may be impossible alone, possible together. We believe that the unique contributions of all Atlassians create our success. To ensure that our products and culture continue to incorporate everyone's perspectives and experience, we never discriminate based on race, religion, national origin, gender identity or expression, sexual orientation, age, or marital, veteran, or disability status. All your information will be kept confidential according to EEO guidelines. To provide you the best experience, we can support with accommodations or adjustments at any stage of the recruitment process. Simply inform our Recruitment team during your conversation with them. To learn more about our culture and hiring process, visit go.atlassian.com/crh . In line with local law, identity verification (which may include use of biometric data) is a condition of employment with Atlassian for employment fraud purposes. At Atlassian, we strive to design equitable, explainable, and competitive compensation programs. We follow consistent hiring practices and account for each candidate's skills, knowledge, and experience when setting base pay within the range. Please visit go.atlassian.com/payzones for more information on which locations are included in each of our geographic pay zones. However, please confirm the zone for your specific location with your recruiter. This role may also be eligible for benefits, bonuses, commissions, and equity.

Requirements

Must-haves

Working at Atlassian
Design and Build ML Systems
Architect and implement scalable systems for training, fine-tuning, and serving large language models and embeddings.
Build efficient retrieval, hybrid search, and RAG pipelines integrated with knowledge-grounded data.
Develop tools and infra to support rapid experimentation, evaluation, and deployment of prototypes.
Enable Rapid Prototyping & Applied Research
Partner with applied scientists to bring new ideas to life in robust, production-ready pipelines.
Build proof-of-concept (POC) systems and evolve them into reliable, scalable services.
Optimize latency, throughput, and resource efficiency for GenAI workloads.
Collaborate Across Disciplines
Work closely with ML engineers, backend developers, and product teams to ship end-to-end innovations.
Contribute to best practices in model deployment, monitoring, and evaluation.
Help establish the team as a world-class hub for GenAI systems innovation.
6+ years in ML systems engineering, backend engineering, or infrastructure roles.
Strong track record of building and scaling ML-powered services in production.
Experience with large-scale model training, inference pipelines, or search/retrieval systems.
Proficiency in backend systems and ML frameworks (Python, PyTorch, TensorFlow, Hugging Face).
Experience with vector databases (Weaviate, Pinecone, FAISS), orchestration frameworks (LangChain, LlamaIndex).
Strong coding skills and ability to optimize systems for performance and reliability.
Familiarity with cloud environments (AWS, GCP, Azure) and container/orchestration tools (Kubernetes, Docker).

Nice-to-haves

Background in distributed systems, high-performance computing, or GPU optimization.
Familiarity with search/GenAI evaluation metrics (e.g., NDCG, groundedness, latency benchmarks).
Experience with monitoring, observability, and reliability practices for ML systems.
Contributions to open-source infra or ML systems frameworks.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningmlpythonawsgcpazurekubernetesdockergo

Platform: atlassian
Effort: medium