FieldAI - Senior Software Engineer, MLOps
Requirements
• 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. • What Will Set You Apart • 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.
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.
Benefits
• Our salary range is competitive with the market, but we take into consideration an individual's background and experience in determining final salary; base pay offered may vary considerably depending on geographic location, job-related knowledge, skills, and experience. Also, while we enjoy being together on-site, we are open to exploring a hybrid or remote option. • We are solving one of the world’s most complex challenges: deploying robots in unstructured, previously unknown environments. Our Field Foundational Models™ set a new standard in perception, planning, localization, and manipulation, ensuring our approach is explainable and safe for deployment.
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