Machine Learning Ops & Infrastructure Engineer

About Noble Machines

Noble Machines (formerly Under Control Robotics) builds multipurpose robots to support human workers in the worlds toughest jobsturning dangerous work from a necessity into a choice. Our work demands reliability robustness and readiness for the unexpectedon time every time. Were assembling a mission-driven team focused on delivering real impact in heavy industry from construction and mining to energy. If youre driven to build rugged reliable products that solve real-world problems wed love to talk.

Position Overview

At noble machines AI we are pushing the boundaries of machine learning and artificial intelligence. To support our rapid pace of innovation we are looking for an experiencedML Ops & Infrastructure Engineer to build the foundational systems that power our AI development.

In this role you will sit at the critical intersection of our Research and Engineering teams. You wont just be maintaining systems; you will be architecting the high-performance ML infrastructure that enables our researchers to seamlessly transition from data collection and model training to evaluation and production. If you are passionate about scalable compute elegant data platforms and robust deployment pipelines we want you on our team.

Responsibilities

• End-to-End ML Infrastructure: Design build and maintain a highly scalable and reliable machine learning infrastructure that accelerates the research and development lifecycle.
• Data Platform & Management: Architect and manage robust data ingestion collection and processing pipelines. You will own the data platforms that ensure our models are trained on high-quality perfectly versioned datasets.
• Training & Evaluation Pipelines: Build and optimize the environments used for distributed model training hyperparameter tuning and automated model evaluation.
• Cloud Compute Orchestration: Manage and orchestrate heavy compute workflows seamlessly across AWS and/or Google Cloud Platform (GCP) optimizing for both performance and cost.
• Containerization & Kubernetes: Take full ownership of containerizing ML workloads and orchestrating them via Kubernetes (K8s) to ensure high availability scalability and reproducibility.
• Cross-Functional Collaboration: Partner closely with ML Researchers and Software Engineers to understand their bottlenecks gather requirements and build tooling that makes their workflows frictionless.

Requirements

• Proven Industry Experience: 3 years of hands-on industry experience building scalable ML infrastructure MLOps platforms or data engineering systems.
• Cloud & Orchestration Mastery: Deep expertise in cloud platforms (AWS or GCP) and modern orchestration tools specifically Docker and Kubernetes (K8s).
• Software Engineering Fundamentals: Strong programming skills in Python alongside experience with bash scripting and version control (Git).
• Data & Pipeline Expertise: Hands-on experience building large-scale data management pipelines and using workflow orchestration tools (e.g. Airflow Argo Kubeflow or similar).
• Relevant Domain Background: While explicit robotics experience is not required we highly value candidates with backgrounds in hardware-interfacing AI autonomous driving computer vision or other high-complexity ML fields.

Nice to Have

• Experience with Infrastructure as Code (IaC) tools like Terraform.
• Familiarity with distributed training frameworks (e.g. PyTorch DDP Horovod Ray).
• Experience implementing model observability monitoring and data drift detection in production environments.
• A background handling large volumes of unstructured data (video sensor data spatial data).

The base salary range for this full-time position is $160000 - $300000 in addition to bonus equity and benefits.

To apply submit your resume here or email .To increase your chances of being selected for an interview we encourage you to include up to TWO examples of your most representative work featuring hardware demonstrations.