Platform Architect, Bellwether, X

P l a t f o r m A r c h i t e c t , B e l l w e t h e r , X

Software Engineering Mountain View, CA (HQ)

About the TeamOur 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 RoleWe're looking for a Platform Architect to define and lead the end-to-end technical architecture of the Bellwether platform. That's the data, ML, and cloud foundation that turns petabytes of Earth observation data into hazard, risk, and damage insights for enterprise and government customers.

As one of Bellwether's most senior technical leaders, you'll own how the whole system fits together:

• how we take in and process planetary-scale geospatial data
• how we train, evaluate, and serve models, from geospatial foundation models to production agentic systems
• how we deliver secure, reliable products and APIs with explainability and observability in the system

This is a hands-on, senior Individual Contributor role. You'll set multi-year technical direction across ML, data, infrastructure, and product engineering at scale, and you'll still prototype, write critical-path code, and review designs yourself. You'll work closely with Bellwether's engineering, product, and business leaders to balance speed with rigor as we grow from early products into an enterprise-grade platform.

How You Will Make a 10x Impact

• Own the Platform Architecture: Define and evolve the end-to-end architecture of the Bellwether platform, along with its multi-year technical roadmap. That covers:
  • Multimodal incl geospatial data management, ingestion and processing
  • ML training and serving
  • foundation-model and agentic integrations
  • customer-facing workflows, APIs, data products and platform
• Set Technical Strategy & Standards: Make the high-stakes calls: build vs. buy, core technology choices, tools integration and platform boundaries. Set up the reference architectures, design-review process, and decision records the team builds on, balancing long-term longevity with rapid prototyping.
• Architect for Planetary-Scale Data: Design fast, cost-efficient and scalable pipelines and storage for petabyte-scale Earth observation data (multi-spectral satellite, Tabular, SAR, LiDAR, and weather/reanalysis data), from raw intake to analysis-ready datasets to support both batch and real time decision making on various downstream tasks.
• Build the ML & Agentic Platform Foundation: Work with ML leads to design the MLOps backbone for continual learning, including distributed training, automated evaluation, drift detection, and data lineage. Build the infrastructure for production agentic systems, with built-in observability and explainability.
• Deliver Enterprise-Grade Products: Design secure, multi-tenant services, APIs, and data-delivery patterns (e.g., hazard maps, data-as-a-service, scenario simulations). They need to meet the security, compliance, and data-isolation expectations of enterprise and government customers.
• Champion Reliability, Security & Cost: Set platform-wide standards for SLOs, observability, zero-trust security, disaster recovery, and FinOps. Work with Cloud Operations to keep the platform reliable and cost-efficient as usage grows.
• Stay Hands-On: Prototype critical components, write and review production code on the critical path, and personally unblock the team's hardest technical problems.
• Multiply the Team: Mentor senior engineers, grow technical leaders, and raise the bar through design reviews, standards, and hiring. Represent Bellwether's architecture with customers, partners, and teams across Google and Alphabet.

What You Should Have

• Education: BS/MS/PhD in Computer Science or a related technical field, or equivalent practical experience.
• Core Experience: 10+ years building and operating large-scale distributed and full stack ML systems (15+ for Principal-level consideration). That includes 5+ years as the architect or technical lead for a platform spanning data, ML, and cloud infrastructure.
• 0-to-1 Platform Track Record: Proven success taking a platform from early stage into robust production at scale, and owning how it evolved as it grew.
• Cloud Architecture Depth: Deep expertise in at least one major cloud (Google Cloud strongly preferred), including Kubernetes/GKE, serverless, networking, IAM, and Infrastructure as Code (Terraform).
• Data Systems at Scale: Experience designing petabyte-scale batch and streaming systems (e.g., Apache Beam/Dataflow, Spark, BigQuery, Pub/Sub or Kafka).
• Production ML Systems: Hands-on experience designing production ML platforms and MLOps (e.g., Vertex AI, Kubeflow, MLflow) across training, serving, evaluation, and monitoring.
• Engineering Craft: Strong, current coding skills in Python plus at least one more language (e.g., Go, TypeScript, Java, or Rust). You still write and review production code.
• Reliability & Security: Track record designing for SLOs, fault isolation, disaster recovery, zero-trust security, and cost efficiency in high-throughput production environments.
• Technical Leadership: Proven ability to set direction across multiple teams and disciplines, influence without authority, mentor senior engineers, and explain architecture trade-offs to executives and customers.
• Comfort with Ambiguity: You do well in a startup-like, high-risk environment where priorities shift and prototypes become products.

It'd be great if you also had these:

• Geospatial & Earth Observation Expertise: Production experience with geospatial/EO data and tooling (e.g., GDAL, rasterio, xarray, Zarr/COG, STAC, Earth Engine).
• Foundation Models & Agentic Systems: Designing platforms for LLM, foundation-model, or agentic applications (agent frameworks, evals, tool use, retrieval).
• Enterprise & Public-Sector Delivery: Building multi-tenant data platforms for enterprise or government customers, including compliance frameworks (e.g., SOC 2, FedRAMP) and customer security reviews.
• Risk & Simulation Workloads: Experience with climate risk, catastrophe/risk modeling, or large-scale simulation workloads.
• Startup / Founding Experience: Early-stage or founding-engineer experience taking a product from prototype to enterprise scale.
• Open Source: Contributions to open-source geospatial, data, or ML infrastructure projects.

The US base salary range for this full-time position is $307,000 - $432,000 + 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.