Job Summary:
FieldAI is transforming how robots interact with the real world by building reliable, risk-aware AI systems for complex challenges in robotics. They are seeking a skilled Senior MLOps Engineer to design and maintain the infrastructure supporting machine learning systems in robotics applications, collaborating with various engineering teams to ensure efficient deployment and monitoring of ML models.
Responsibilities:
• Design, build, and maintain GPU based infrastructure for machine learning pipelines, including data processing, training, evaluation, inference and deployment workflows.
• Collaborate closely with robotics teams to implement model serving infrastructure for edge/robot deployment.
• Build tools and automation to support reproducible experiments, model versioning, and dataset management.
• Deploy and manage ML services and inference pipelines using containerized environments for efficient scaling and scheduling of heterogeneous compute resources.
• Monitor model performance and system reliability across development and production environments.
• Improve the efficiency, scalability, and reliability of ML workflows and infrastructure.
• Work with cross-functional engineering teams to integrate ML components into robotics software systems.
Qualifications:
Required:
• Bachelor’s degree in Computer Science, Engineering, or a related field (or equivalent work experience).
• 3-7 years of experience in MLOps, machine learning infrastructure, or related engineering roles.
• Strong programming skills in Python or similar languages.
• Experience building and maintaining machine learning pipelines.
• Hands-on experience with cloud and cloud-native tools such as AWS (SageMaker, S3, or similar cloud ML services), Kubernetes etc.
• Solid understanding of Linux systems and distributed computing environments.
• Experience with GPU workload scheduling and orchestration across multi-region cloud environments.
• Excellent problem-solving skills and the ability to work collaboratively in a team environment.
Preferred:
• Experience deploying and operating ML systems for robotics or real-world physical systems.
• Experience with scaling AI, ML, and inference workloads on Kubernetes.
• Exposure to ROS-based robotics data formats and pipelines (rosbags, point clouds)
• Experience with experiment tracking, model versioning, or dataset versioning tools.
• Experience optimizing ML pipelines for large-scale training and data processing.
• Experience working closely with research or applied machine learning teams.
Company:
FieldAI is building general robot intelligence for the physical world. Founded in 2023, the company is headquartered in Mission Viejo, USA, with a team of 201-500 employees. The company is currently Growth Stage.