Machine Learning Operations Engineer

About the position

We are seeking a mid-level Machine Learning Operations Engineer to join our growing team. In this role, you will focus on unifying data management at Lila by building and maintaining high performance and robust data pipelines to support a variety of machine learning use-cases. You will work closely with both LLM researchers and Applied AI Engineers to ensure the seamless integration of cutting-edge LLM research with scalable, production-ready systems for life science and physical science automation.

Responsibilities

• Design and implement high-performance data processing infrastructure for large language model training
• Collaborate with researchers to implement novel data processing pipelines
• Develop an easy-to-use, secure, and robust developer experience for researchers and engineers
• Contribute to the MLOps best practices at Lila Sciences and write technical documentation for staff

Requirements

• 3+ years of experience in software engineering, with a focus in data engineering or DevOps
• Demonstrated experience deploying and maintaining machine learning models in production
• Proficiency with Kubernetes, Docker, and Cloud (AWS Preferred)
• Proficiency with CI/CD tools and Frameworks (GitHub Actions preferred)
• Strong skills with Scripting languages (e.g. Python, Bash), VCS (git), and Linux
• Proven experience in cross-functional teams and able to communicate effectively about technical and operational challenges.

Nice-to-haves

• Proficiency with scalable data frameworks (Spark, Kafka, Flink)
• Proven Expertise with Infrastructure as Code and Cloud best practices
• Proficiency with monitoring and logging tools (e.g., Prometheus, Grafana)
• Experience managing on-premises kubernetes environments (e.g. Rancher)