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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Lead - Geospatial, Bellwether - **Company:** Google LLC - **Location:** Mountain View, CA, United States - **Experience:** Expert - **Salary:** $197,000.0 - $311,000.0 - **Contract:** Permanent contract - **Skills:** Geographic Information Systems, Agile Methodology, Google App Engines, Software Design Patterns, Data Flow Control, Python (Programming Language), Machine Learning, NumPy, Open Source Technology, Pair Programming, Software Architecture, Rapid Prototyping Process, Tensorflow, SciPy, Software Deployment, Software Engineering, Systems Architecture, Google Cloud, Pytorch, Software Troubleshooting, Keras, Git, Pandas, Information Technology, Code Testing, Machine Learning Operations, Software Coding, Apache Beam - **Published:** August 7, 2026 - **Apply:** https://www.juju.com/job/00000000glyevl ## About the Role + Master's degree or PhD in Computer Science, Applied Mathematics, Physics, or equivalent practical experience + 7 years experience with the machine learning development pipeline: research, experimentation, and ML-Ops + Experience in engineering management or as a technical lead, with a track record of guiding team strategy and mentoring engineers while remaining hands-on. + Expertise in ML frameworks (e.g., PyTorch, TensorFlow/Keras/JAX) and Python libraries (e.g., NumPy, SciPy, Pandas). + Experience with numerous common software design patterns (for example, Observer, Decorator, Visitor, Producer/Consumer, etc). + Experience with open source tools such as: Git, TensorFlow, Apache Beam/Dataflow, Google Compute Engine. + Python proficiency. + Experience working on an early stage project and environment where prototype technologies are evolved into a production phase. + An ability to thrive in an Agile-driven team: iteratively sprinting toward goals and products, contributing new ideas, standards, and processes. + Experience interfacing with customers It'd be great if you also had these: + Production-level experience in the geospatial industry, with a wide variety of tasks, including code development, designing for, implementing, and managing security measures and controls, troubleshooting and debugging, designing and implementing code testing processes, and monitoring deployed application's performance and health. + Experience working with a wide variety of geospatial data + Experience in Machine Learning Operations - scaling existing machine learning applications into production ## Description You will be a hands-on Machine Learning engineer, contributing to all aspects of the project's development and deployment of applications. We are looking for a motivated expert level ML Engineer with broad experience across systems architecture and design in one or more cloud platforms. This role would guide machine learning for Bellwether's real-world products, and is not a research-based role. Our team is small but mighty and highly collaborative, and values pair programming and cooperative ideation. We are committed to agile principles and rely heavily on this framework for efficient sprints and cycles. We are looking for passionate and driven people, who are comfortable moving between creative, big-picture thinking and specifics of how to execute. We operate in a fast-paced, fluid environment as our team moves from early stage development into production phases. How you will make 10x impact: + Embracing your ability to be a ML 'expert generalist', enjoying the fluidity of moving from architecting new production systems, to machine learning, to security and monitoring. From high level strategy to specific tactics. + Help drive and execute key decisions on software architecture and features, balancing business needs and our technology roadmap, balancing longevity with rapid prototyping + Contribute as a key team member to the creation of new systems and processes to ensure high quality development, deployment, and maintenance of live applications in production environments + Create and maintain Google Cloud Platform-based infrastructure for software development and high-volume production systems. + Present findings to team members, internal and external stakeholders, and help set a direction for future development. ## Related Videos - [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs) - [Vectorize all the things! 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