Staff Machine Learning Engineer

Build the Future Workforce Wand turns AI into labor. It enables humans and AI agents to operate together as a unified, hybrid workforce, with comprehensive management and oversight. And it’s already operating at scale inside some of the world’s largest organizations. Wand built the world’s first Agentic Labor Infrastructure enabling governments and global enterprises to create, manage, and scale digital workforces. Our mission is to integrate agent ecosystems into the core of work and business, unlocking a generational leap in the global economy. We’re building the infrastructure that lets humans and AI agents operate together safely, transparently, and at scale. Join Wand in leading the Agentic Shift Wand is building a high-performing global team who take full ownership of what they build. We lead by example, move fast, make data-aware decisions, and continuously push for more- always with a focus on delivering real value to customers. You would be joining a world-class team that combines deep research expertise and real-world product execution, with experience spanning Deepmind, Google, Amazon, Miro, Elise AI, IBM and Accern. Position Summary: We are hiring a highly experienced Staff Machine Learning Engineer to serve as a senior technical leader within our engineering organization. This is a deeply hands-on individual contributor role focused on designing and evolving AI systems that act autonomously, drive product goals, and operationalize business logic. You will lead the development of scalable ML infrastructure, agentic workflows, and data pipelines that enable AI agents to make decisions, execute tasks, and deliver measurable product outcomes. The role requires deep expertise in machine learning engineering, distributed systems, MLOps, and agentic AI architectures. You will collaborate across platform, data, product, and engineering teams to ensure AI agents can be reliably deployed, monitored, and integrated into business-critical workflows. As a Staff Engineer, you will shape the long-term architecture of ML systems, define standards for agentic AI, and drive the productization of AI capabilities across the organization. Role Responsibilities: 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. Key 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. Preferred Experience: Experience building ML platforms that enable AI agents to drive product outcomes and autonomous workflows. Experience with NLP, LLMs, generative AI, or multi-agent systems. Experience building feature stores or shared ML infrastructure supporting agentic reasoning and coordination. Experience operating ML workloads on Kubernetes-based infrastructure. Experience designing systems for real-time goal-driven inference and autonomous execution at scale. Experience building ML systems in enterprise SaaS or large-scale product environments. Experience supporting AI capabilities in regulated or enterprise domains. Experience with large-scale data platforms, streaming architectures, and agent orchestration pipelines. Experience evaluating ML infrastructure tools for production agentic AI workflows. Personal Characteristics: Strong systems thinker who understands interactions across ML, data, infrastructure, agentic workflows, and product logic. High ownership mentality with accountability for the reliability of autonomous AI systems. Strong problem solver who anticipates operational, product, and agentic failure modes. Comfortable influencing technical and product strategy across teams without formal authority. Collaborative mindset with the ability to work across data science, engineering, product, and platform teams. Learning-oriented, passionate about staying at the forefront of agentic AI, ML systems, and product-driven AI workflows. Calm and methodical when diagnosing complex ML, agentic, or production system issues.