Machine Learning Engineer, CloudBees

• 4+ years of experience in machine learning engineering or applied ML roles
• Hands-on experience with Amazon Bedrock and/or Google Vertex AI for deploying and customizing foundation models
• Strong understanding of ML lifecycle management and workflow orchestration (e.g., Airflow, Kubeflow, MLflow)
• Experience with Python, TensorFlow, PyTorch, or similar ML frameworks
• Proficiency in cloud platforms (AWS and/or GCP) and containerized environments (e.g., Docker, Kubernetes)
• Familiar with the modern LLM training stack (eg. the Hugging Face ecosystem, PEFT, etc.)
• Experience in a startup or fast-paced product development environment
• Familiarity with version control, CI/CD, and infrastructure as code (e.g., Terraform, GitHub Actions)
• Experience with evaluations
• LLM - large language model
• As a member of the AI Foundations founding team, this is a high-impact role that sits at the intersection of artificial intelligence and software delivery – ideal for someone passionate about pushing the boundaries of developer productivity and intelligent automation

Desirable

• CloudBees is seeking a Machine Learning Engineer with hands-on experience in generative AI platforms like Amazon Bedrock and Google Vertex AI, as well as expertise in building and maintaining scalable ML pipelines. In this role, you will design, implement, and optimize ML workflows and models that support intelligent automation and insights across the CloudBees product suite
• You’ll be part of a fast-paced, collaborative, and forward-thinking team, with the opportunity to make a direct impact on product direction and user experience. With the rapidly changing AI ecosystem, being able to apply the right tool at the right time and identify areas the team needs to invest in is critical
• Design, build, and maintain production-ready ML pipelines, from data ingestion to model deployment and monitoring
• Integrate and fine-tune foundation models and generative AI services using Amazon Bedrock, Google Vertex AI, or similar platforms
• Collaborate with software engineers, product managers, and data scientists to deliver intelligent features embedded in DevOps tools
• Automate model retraining, versioning, testing, and CI/CD processes across environments
• Implement observability and reliability into ML systems, ensuring scalability and performance
• Contribute to the technical roadmap for AI/ML development within the CloudBees platform
• Document systems and share best practices across teams