Staff Machine Learning Engineer

Role Overview

• Architect and lead the development of scalable ML platforms that support autonomous, goal-driven AI agents.
• Design systems that support the full ML lifecycle, including agentic decision-making, task orchestration, and automated goal execution.
• Build frameworks for integrating models with product logic, business objectives, and operational workflows.
• Lead the development of pipelines that enable experimentation, productionization, and continuous agentic learning.
• Define architecture standards and engineering practices for agentic AI, goal alignment, and productized ML solutions.
• Collaborate with data science and product teams to turn research outputs into production AI agents that drive real product impact.
• Design infrastructure supporting large-scale training, inference, and multi-agent coordination workloads.
• Strengthen observability and monitoring across pipelines, AI agents, and goal-driven behavior execution.
• Implement systems for automated evaluation, goal alignment checks, drift detection, and retraining.
• Improve reliability, scalability, and operational excellence of ML services powering autonomous workflows.
• Lead troubleshooting of complex agentic system failures and distributed ML infrastructure issues.
• Influence CI/CD and development workflows supporting ML lifecycle, agent orchestration, and automated deployment.
• Mentor engineers to build expertise in agentic systems, AI-driven product logic, and autonomous workflows.
• Collaborate with architects and senior engineers to shape long-term AI platform strategy and agentic product roadmaps.

Requirements

• Extensive hands-on experience building production ML systems integrated with product goals and business logic.
• Deep expertise in agentic AI, ML engineering, and MLOps practices.
• Strong programming skills in Python and experience integrating ML with backend systems and autonomous workflows.
• Proven experience deploying machine learning models at scale, including goal-driven or multi-agent systems.
• Experience building ML infrastructure for training, experimentation, inference, and agent coordination.
• Strong understanding of distributed systems, scalable data pipelines, and real-time agentic decision loops.
• Experience designing ML systems on cloud platforms such as AWS, Azure, or GCP.
• Experience building highly available model serving systems supporting autonomous agentic tasks.
• Ability to influence architecture and product integration decisions across engineering teams.
• Strong debugging and troubleshooting skills in complex production ML and agentic AI environments.
• Ability to lead complex technical initiatives without formal management authority.
• Excellent communication skills to work effectively across engineering, product, and data science teams.

Tech Stack

• AWS
• Azure
• Cloud
• Distributed Systems
• Google Cloud Platform
• Python

Benefits

• Professional development opportunities