Machine Learning Engineer

Senior Machine Learning Engineer:

DESCRIPTION

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.

REQUIREMENTS

Position Summary:

We are hiring a highly skilled Senior Machine Learning Engineer to build AI systems that act autonomously, drive product goals, and integrate business logic. This hands-on role focuses on implementing agentic workflows, scalable ML infrastructure, and data pipelines that enable AI agents to make decisions, execute tasks, and deliver measurable product outcomes.

You will collaborate with platform, data, product, and engineering teams to productionize AI research, operationalize agentic models, and integrate autonomous workflows into real-world products. This role emphasizes execution, technical impact, and operational excellence in agentic AI systems.

Role Responsibilities:

Develop and maintain ML platforms and pipelines supporting autonomous, goal-driven AI agents.

Build systems for the full ML lifecycle, including agentic decision-making, task orchestration, and goal execution.

Integrate ML models with product logic and business workflows to operationalize AI capabilities.

Implement pipelines for experimentation, productionization, and continuous agentic learning.

Collaborate with data science and product teams to turn research outputs into production AI agents.

Design and optimize infrastructure for large-scale training, inference, and multi-agent coordination.

Implement observability and monitoring for ML pipelines, agent behaviors, and goal-driven execution.

Build systems for automated evaluation, drift detection, and retraining of AI models.

Ensure reliability, scalability, and operational excellence of ML services powering autonomous workflows.

Troubleshoot complex issues in ML pipelines, agentic systems, and distributed infrastructure.

Contribute to CI/CD and development workflows supporting ML lifecycle, agent orchestration, and model deployment.

Collaborate and share knowledge to improve implementation of agentic AI systems across teams.

Key Requirements:

Hands-on experience building production ML systems integrated with product goals and business logic.

Expertise in ML engineering, agentic workflows, and MLOps practices.

Strong programming skills in Python and experience integrating ML with backend systems and autonomous workflows.

Experience deploying machine learning models at scale, including goal-driven or multi-agent systems.

Experience building ML infrastructure supporting training, experimentation, inference, and agent coordination.

Solid 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 Google Cloud Platform.

Experience with highly available model serving systems supporting autonomous agentic tasks.

Strong debugging and troubleshooting skills in complex ML and agentic AI production environments.

Ability to work independently and collaboratively within cross-functional 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 at scale.

Experience building ML systems in enterprise SaaS or large-scale product platforms.

Experience supporting AI capabilities in regulated or enterprise environments.

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 and accountability for reliable AI systems.

Strong problem solver who anticipates operational, product, and agentic failure modes.

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 and product-driven ML systems.

Calm and methodical when diagnosing complex ML, agentic, or production system issues.