About the Team:
Our team – Bellwether – operates at the intersection of machine learning, geospatial data, and pressing issues with enterprise customers, governments, and more. We focus on severe weather manifestations of climate change, such as wildfires, and aim to use a wide range of data and analytics to better understand and predict what these events could mean for communities and businesses across the globe. We do this through the detection and classification of the features and patterns of the natural and built worlds, the application of cutting-edge machine learning, and the redesign of the full geo-ML workflow. This work extends into development and production-level work as well, as Bellwether also has a number of products that are live with customers. The result: tools and models that allow a range of industries to better leverage earth observation insights.
About the role:
You will be a hands-on Machine Learning engineer, contributing to all aspects of the project’s development and deployment of applications. 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:
- 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.
- Collaborate with team members, internal and external stakeholders, and help execute on the direction for future development.
What you should have:
- PhD in Computer Science or equivalent practical experience
- 7 years experience with the machine learning development pipeline: research, experimentation, and ML-Ops
- 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
The US base salary range for this full-time position is $174,000 - $255,000 + bonus + equity + benefits. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your location during the hiring process.
Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits.
Top Skills
What We Do
We create breakthrough technologies to help solve some of the world’s biggest problems. Born at Google, we got our start creating self-driving cars and smart glasses. Since then, we’ve continued to bring sci-fi ideas into reality.
