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# Principal Machine Learning Systems Engineer (GenAI Products & Knowledge Innovations)

- **Company:** Atlas Data Systems, L. L.C.
- **Location:** San Francisco, CA, United States
- **Experience:** Expert
- **Salary:** $236,700.0 - $309,025.0
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Automated Storage and Retrieval Systems, Microsoft Azure, Program Optimization, Distributed Systems, Python (Programming Language), Machine Learning, Open Source Technology, Tensorflow, Google Cloud, Cloud Platform System, High Performance Computing, Pytorch, Delivery Pipeline, Large Language Models, Backend, Kubernetes, Information Technology, HuggingFace, Atlassian Tools, Machine Learning Operations, Software Coding, Docker
- **Published:** September 18, 2026
- **Apply:** https://www.themuse.com/jobs/atlassian/principal-machine-learning-systems-engineer-genai-products-knowledge-innovations

## About the Role

* 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)., * Bachelor's or Master's in Computer Science, Machine Learning, or related fieldor 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.

## Description

* 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., 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.


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