Machine Learning Operations Engineer

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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.

As an MLOps Engineer at Field AI, you will play a pivotal role in ensuring the scalability, efficiency, and reliability of our machine learning systems. Our company is at the forefront of robotics innovation, with a global fleet of robots generating vast amounts of data. Your work will directly impact how we manage and utilize this data to optimize the performance of our robots and drive innovation across industries.

You will work alongside a collaborative team of data scientists, software engineers, and robotics experts, helping to bridge the gap between machine learning models and production systems. While your primary focus will be on developing and maintaining robust ML infrastructure and pipelines, you will also assist with model deployment, ensuring that models are integrated smoothly and perform optimally in live environments.

This role offers the opportunity to work with cutting-edge technologies, solve complex problems, and contribute to the success of large-scale, real-time data systems. You’ll be key in managing large data flows, and ensuring that our robots continue to operate seamlessly and efficiently worldwide.

• Machine Learning Infrastructure & Data Pipelines
• Collaborate with data scientists and software engineers to design and build scalable machine learning infrastructure that supports the data generated by our global robot fleet
• Manage and optimize large-scale data pipelines that handle continuous streams of data from robots deployed worldwide
• Develop and implement strategies for model versioning, reproducibility, and efficient retraining workflows
• Leverage cloud infrastructure (AWS, Azure, GCP) to support model training, deployment, and monitoring at scale

• Model Deployment, Monitoring & Performance
• Assist with deploying machine learning models into production environments, working closely with the data science team to ensure smooth integration and performance
• Automate and streamline the monitoring and maintenance of machine learning models in production
• Continuously monitor models in production, detecting model drift and automating retraining processes as necessary
• Troubleshoot issues related to model deployment, performance, and system integration

• Systems Optimization & Troubleshooting
• Ensure seamless integration of machine learning models into production systems, optimizing for scalability, reliability, and performance
• Work to identify and resolve complex system performance issues related to model deployments, data pipelines, and cloud infrastructure
• Support the development of system architecture strategies to improve ML model deployment workflows and cloud infrastructure performance
• Maintain and optimize CI/CD pipelines for machine learning workflows to ensure continuous delivery of reliable models

• 3+ years of relevant experience in MLOps, Dev Ops, or a similar role, preferably within a robotics or data-intensive environment
• Strong understanding of machine learning frameworks (e.g., Tensor Flow, PyTorch, Scikit-learn)
• 3+ years of hands-on experience with containerization (e.g., Docker, Kubernetes) and orchestration tools
• Familiarity with cloud-based platforms for machine learning (AWS, Azure, GCP)
• Experience with building and maintaining CI/CD pipelines for machine learning workflows
• Proficiency in version control tools such as Git
• Strong understanding of system architecture, software development practices, and how they relate to ML model deployment

• Experience working with large-scale data systems, particularly those involving real-time data streams from sensors and robots
• Familiarity with ML deployment platforms…