At NobleAI, we believe that material science and chemistry are key to building a sustainable world and that artificial intelligence is essential to unlock this potential. NobleAI leverages innovative Science-Based AI technology to revolutionize energy workflows, materials development, and chemical designs. We enable companies to accelerate innovation and reduce costs in developing sustainable technologies and products.

We're a team of excellence-driven individuals, valuing thoughtfulness and respect while focusing on delivering products that empower engineers and researchers to create better solutions faster.

As a Machine Learning Engineer (MLOPS) at NobleAI, you will deliver the capability to deploy models and data using cloud-managed and open-source toolset and services. The ML Engineer with an MLOps focus is responsible for working closely with research and engineering teams to design and enable platform features that allow batch and real-time inferences. You will also provide engineering best practices and create templates and self-service modules to accelerate research scientists and other ML engineers to automate and shorten the path to production deployments. You will build configurable, scalable software modules that can be used to standardize deployments for batch and real-time requirements.

Join us in building a more sustainable world through the power of AI and scientific innovation.

Requirements

Key Responsibilities

Design, build, and maintain scalable and resilient MLOPS architecture and code across our platform codebase, Kubernetes/KServe, AWS, and Azure
Collaborate with Research Scientists and DevOps Engineers to deploy custom models on the platform or as independent services
Help debug and resolve issues with model or service performance
Provide oversight/guidance and templates for Research Scientists to self-serve ML deployments for non-production needs
Deploy data assets and pipelines for model inference endpoints

What We're Looking For

MSc Degree in Computer Science or a related field
Understanding of machine learning algorithms and techniques
3+ years of experience in building and deploying ML systems
3+ years of experience working with Python and MLOps tools, including Docker, Kubernetes, KubeFlow, TensorFlow, PyTorch, Sagemaker, MLFlow
2+ years of cloud experience (AWS or Azure ML Platforms)
3+ years of experience in Software Engineering practices such as version control, testing, DevOps (build pipelines, CI/CD), and Python package management
Demonstrated ability to communicate complex technical details at a high level effectively

Benefits

Did we mention we offer great pay & benefits? 

Top tier health benefits coverage including medical, dental, vision, disability and life insurance
Generous Paid Time Off & Holidays
Remote workforce with access to co-working offices
401(k) plan with employer match
Equity package
Performance-based bonus plan
Base Salary Range $140k - $180k per year, depending on experience and geographic location

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