We’re looking for an exceptional Senior ML/AI Engineer to join our core engineering team and help build the intelligence layer of our platform. In this role, you’ll design, develop, and operate machine learning systems that analyze workload behavior, predict infrastructure needs, and drive automated workload execution decisions for customer environments.

This is a high-impact role with significant influence over our ML architecture, model development process, and AI-driven product capabilities.

What You’ll Do

• Design, build, and deploy ML models that autonomously manage data workloads
• Develop systems that learn from workload behavior, seasonality, infrastructure usage, and runtime patterns
• Build production ML pipelines for training, evaluation, deployment, monitoring, and continuous improvement
• Collaborate with backend, data, and product teams to turn modeling ideas into reliable product capabilities
• Evaluate model performance using real-world production data and define clear success metrics
• Design features, signals, and model architectures that support automated decision making
• Own ML systems in production, including monitoring, drift detection, performance analysis, and reliability
• Help establish engineering standards for production-grade ML and AI systems

What We’re Looking For

We’re looking for someone who can bridge applied machine learning and production engineering. You should be comfortable building models, but also understand what it takes to operate them reliably in real customer environments.

Specifically, we’re looking for candidates who have:

• At least 4 years of experience in machine learning, AI engineering, applied data science, or related engineering roles
• Strong proficiency in Python and modern ML libraries such as scikit-learn, PyTorch, TensorFlow, XGBoost, or similar
• Experience developing and deploying ML models in production
• Strong understanding of model evaluation, feature engineering, data pipelines, and experimentation
• Experience working with time-series, infrastructure, or operational data
• Ability to design reliable ML systems that integrate with production software
• Strong software engineering fundamentals, including code quality, testing, APIs, and maintainability
• Comfortable taking ownership and making decisions in a fast-paced environment

Why Zipher?

• A team that values strong technical craftsmanship, clear thinking, and initiative
• Join early and shape the technical direction of the company
• High ownership, high trust, and meaningful impact from day one