About the role
Field AI is transforming how robots interact with the real world. We are building risk-aware, reliable, and field-ready AI systems that address the most complex challenges in robotics, unlocking the full potential of embodied intelligence. We go beyond typical data-driven approaches or pure transformer-based architectures, and are charting a new course, with already-globally-deployed solutions delivering real-world results and rapidly improving models through real-field applications.
We are seeking a skilled and motivated Senior MLOps Engineer to join our engineering team. In this role, you will design and maintain the infrastructure and tooling that supports the full lifecycle of machine learning systems used in robotics applications. You will work closely with machine learning engineers, robotics engineers, and infrastructure teams to ensure reliable training, evaluation, deployment, and monitoring of ML models. This is an exciting opportunity to help operationalize machine learning in real-world robotic systems within a fast-growing and dynamic environment.
What You Will Get To Do
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Design, build, and maintain GPU based infrastructure for machine learning pipelines, including data processing, training, evaluation, inference and deployment workflows.
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Collaborate closely with robotics teams to implement model serving infrastructure for edge/robot deployment.
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Build tools and automation to support reproducible experiments, model versioning, and dataset management.
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Deploy and manage ML services and inference pipelines using containerized environments for efficient scaling and scheduling of heterogeneous compute resources.
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Monitor model performance and system reliability across development and production environments.
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Improve the efficiency, scalability, and reliability of ML workflows and infrastructure.
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Work with cross-functional engineering teams to integrate ML components into robotics software systems.
What You Have
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Bachelor’s degree in Computer Science, Engineering, or a related field (or equivalent work experience).
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3-7 years of experience in MLOps, machine learning infrastructure, or related engineering roles.
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Strong programming skills in Python or similar languages.
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Experience building and maintaining machine learning pipelines.
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Hands-on experience with cloud and cloud-native tools such as AWS (SageMaker, S3, or similar cloud ML services), Kubernetes etc.,
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Solid understanding of Linux systems and distributed computing environments.
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Experience with GPU workload scheduling and orchestration across multi-region cloud environments.
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Excellent problem-solving skills and the ability to work collaboratively in a team environment.
What Will Set You Apart
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Experience deploying and operating ML systems for robotics or real-world physical systems.
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Experience with scaling AI, ML, and inference workloads on Kubernetes.
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Exposure to ROS-based robotics data formats and pipelines (rosbags, point clouds)
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Experience with experiment tracking, model versioning, or dataset versioning tools.
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Experience optimizing ML pipelines for large-scale training and data processing.
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Experience working closely with research or applied machine learning teams.
