M a c h i n e L e a r n i n g E n g i n e e r , B e l l w e t h e r , XSoftware Engineering Mountain View, CA (HQ)
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'll be a hands-on Senior Machine Learning Engineer, contributing across the full lifecycle of building and deploying our applications, from prototyping to production. We're looking for an experienced ML engineer with a strong background in ML in production and MLOps framework for enabling continual learning. This is a highly technical Individual Contributor role focused on building real-world products, not pure research.
Our team is small but mighty and highly collaborative. We value pair programming and coming up with ideas together. We follow agile principles and rely on them to run efficient sprints and cycles. We're looking for people who are comfortable moving between big-picture creative thinking and the details of execution. We work in a fast-paced, fluid environment as the team moves from early-stage development into production.
How You Will Make a 10x Impact - Be an ML "Expert Generalist": Enjoy the fluidity of moving from architecting new ML models to production grade ML agentic systems instrumented for observability and explainability. Translate high-level strategy into specific, tactical engineering execution.
- Drive Architecture Decisions: Help drive and execute key decisions on ML pipeline architecture and features, balancing business needs with our technology roadmap, and balancing long-term system longevity with rapid prototyping.
- Build Production Systems: 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 for agentic platforms.
- Manage Cloud Infrastructure: Create and maintain Google Cloud Platform-based ML infrastructure for software development and high-volume production systems.
- Collaborate & Communicate: Present findings to team members, internal and external stakeholders, and help set the strategic direction for future development.
What You Should Have - Education: BS/MS in Computer Science or equivalent practical experience.
- Core Experience: 5+ years working across the ML development pipeline (ML experimentation, ML development and MLOps), including scaling ML applications into robust production environments.
- ML Frameworks: Strong experience with ML frameworks (e.g., PyTorch, TensorFlow, JAX) and Python libraries (e.g., NumPy, SciPy, Pandas).
- Open Source & Cloud Tooling: Experience with tools and infrastructure such as Git, Apache Beam/Dataflow, Google Compute Engine and Vertex AI.
- Software Engineering Craft: Strong Python skills, plus experience in an early-stage environment where prototypes evolve into production systems.
- Diagnostic Craft: Hands-on experience troubleshooting, debugging, designing code testing processes, and monitoring the performance and health of deployed applications.
- Agile Mindset: Ability to thrive in an Agile-driven team: iteratively sprinting toward goals and products, while actively contributing new ideas, standards, and processes.
- Scaling & MLOps: Deep experience in Machine Learning Operations (MLOps) - distributed training, automated eval, model retrain/ monitoring/drift detection, data lineage, high throughput, dynamic batching etc.
It'd be great if you also had these: - Agentic/LLM Experience: Building LLM-based or agentic systems (agent frameworks, evals, tool use).
- Geospatial & Earth Observation Expertise: Production experience working with and modeling geospatial and Earth observation data (e.g., GDAL, rasterio, Earth Engine).
- Security in Production: Designing, implementing and managing security measures and controls.
- Infrastructure Tooling: Docker, ML flow, KubeFlow, Kubernetes/GKE, Terraform.
The US base salary range for this full-time position is $174,000 - $261,000 + 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.
