TELECOMMUTE Principal Machine Learning Systems Engineer

• 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., 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.

• 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, Google Cloud Platform, 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.

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.

Pay Ranges

In The United States, we have three geographic pay zones. For this role, our current base pay ranges for new hires in each zone are:

Zone A: $236,700 - $309,025

Zone B: $213,030 - $278,123

Zone C: $196,461 - $256,491

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.