Staff Full Stack Engineer

Pravāh•San Francisco, CA

17h•Onsite

About The Position

Pravāh is building foundational intelligence for the electric grid, so that utilities and energy companies can better predict supply, demand, grid volatility, and extreme weather. We’re looking for founding team members to scale our platform globally. We’re negotiating multi-million dollar contracts with some of the largest utilities in the world, have raised $7.5M from three of Silicon Valley’s top VCs and are actively working on scaling our platform in three countries. But most importantly, we’re solving a problem with global impact - all while having fun working every single day. If you care about solving global problems, have high agency, and want to work with some of the most energetic and brilliant people in the world, join us :) This role is extremely important for us, and we want to be honest with what type of people will succeed in this team. We took the time to write out a much more detailed note on our founding roles here: https://pravah.notion.site/ Role Summary As a Staff Full Stack Engineer, you will define the technical architecture and engineering direction of Pravāh's AI-driven grid intelligence platform. You'll make the foundational design decisions — across frontend, backend, and infrastructure — that determine how the platform scales from serving a handful of utility clients to dozens across multiple countries. You'll work across the entire stack: building high-performance web applications that visualize time-series and geospatial data, designing scalable APIs and data pipelines that integrate weather, demand, and grid topology datasets, and productionizing machine learning models in collaboration with our power systems and ML teams. You'll also shape our core infrastructure — CI/CD, observability, and deployment patterns — from the ground up. This is an early engineering role. You'll have a direct impact on a platform that already serves utilities managing power for over 300 million customers across three countries. If you want to build critical infrastructure at the intersection of AI and energy — with real contracts, real data, and real-world consequences — this is the role.

Requirements

Bachelor's degree in Computer Science, Software Engineering, or equivalent practical experience.
8+ years of experience building and deploying production-grade software systems, with meaningful full-stack breadth across frontend, backend, and infrastructure.
Demonstrated ability to drive technical strategy and lead architectural decisions that span multiple teams or system boundaries — not just individual features.
Proficiency in TypeScript and at least one modern frontend framework (Angular strongly preferred; React also considered), with deep understanding of state management, reactive patterns, and performance optimization at scale.
Strong backend development experience in Python, Java/Kotlin, or Go, with expertise in designing systems for high throughput, reliability, and maintainability.
Extensive experience with microservices architecture, including RESTful APIs, gRPC, event-driven design patterns, and message queues — with opinions on when each pattern is and isn't appropriate.
Hands-on experience with Google Cloud Platform (preferred) or AWS/Azure, including architecture-level decisions around managed services, networking, security, and cost optimization.
Working experience with Cloud SQL (PostgreSQL/MySQL) and familiarity with time-series data storage patterns at scale.
Experience with Docker, containerized deployments, and CI/CD pipeline design.
Knowledge of logging and monitoring tools (Prometheus, Grafana, GCP Cloud Monitoring) for building production observability, not just using it.
Experience mentoring engineers and establishing engineering practices — code review culture, testing standards, architectural documentation.
Track record of operating with high autonomy and driving outcomes in ambiguous, fast-moving environments.

Nice To Haves

Experience as a technical lead or staff+ engineer at a high-growth startup or within a larger organization's "zero-to-one" initiative.
Experience interfacing directly with enterprise customers on technical requirements, system design, or integration architecture.
Experience designing multi-tenant SaaS platforms with data isolation, client-specific configuration, and compliance requirements for regulated industries.
Experience with D3.js, Mapbox, Deck.gl , or WebGL for rendering complex geospatial or time-series visualizations.
Experience building data-intensive applications that process and visualize large-scale time-series datasets (millions of records, sub-minute resolution).
Working knowledge or background in power systems, electric grid technologies, or energy markets.
Experience with GIS data formats (shapefiles, GeoJSON, CIM models) and spatial analysis.
Familiarity with ML model serving — deploying and monitoring inference pipelines in production, model versioning, or feature stores.
Experience with enterprise security architectures, compliance standards, or building multi-tenant SaaS platforms for regulated industries.
Track record in high-growth or "zero-to-one" environments where you've built core infrastructure from scratch.
Experience designing reusable component libraries and managing complex frontend build pipelines (Webpack, Vite, or Nx).

Responsibilities

Architect the frontend platform and establish the patterns — state management, component architecture, data fetching, and caching strategies — that other engineers build on across multiple utility-facing products.
Design and implement the most technically challenging frontend systems: high-performance geospatial visualization layers using libraries like Mapbox, Deck.gl , or D3.js to render distribution network topology, feeder-level load profiles, and spatial weather overlays on utility grid maps.
Build real-time, dynamic dashboards for day-ahead and intraday energy demand forecasting — rendering large time-series datasets (millions of data points across thousands of feeders) with smooth, responsive interactions.
Profile and optimize rendering performance for data-heavy views.
Create map-based views that allow utility engineers to drill from substation-level down to individual distribution transformers, supporting bottleneck identification, fault isolation, and capacity planning.
Define and maintain the shared design system and reusable component library to ensure UI consistency across multiple internal and external applications.
Set standards for accessibility, responsiveness, and pixel-accuracy in translating Figma designs.
Establish frontend testing strategy and standards using frameworks like Jest, Cypress, or Playwright — ensuring reliability of mission-critical tools that utilities depend on for operational decisions.
Make technology and tooling decisions for the frontend build pipeline (Webpack, Vite, or Nx), optimizing for developer experience, build performance, and long-term maintainability.
Design the backend architecture for scalable, multi-tenant data processing and ML model serving — defining service boundaries, data modeling patterns, and API contracts that the team extends as new utility clients and use cases are onboarded.
Architect and build data ingestion pipelines that process high-frequency time-series data at scale — including 5-minute interval weather forecasts from multiple providers (XWeather, OpenMeteo), AMI meter data, SCADA telemetry, and grid topology datasets — normalizing across inconsistent formats, time zones, and data quality levels.
Design the integration layer between backend services and ML inference pipelines — serving TiDE, transformer-based, and ensemble forecasting models that consume 1000+ feature covariates including weather, calendar effects, and grid state variables.
Architect model versioning, A/B testing, and automated retraining workflows for production reliability.
Build and own services that manage network metadata and grid topology — ingesting GIS shapefiles, CIM models, and utility asset registers to support load-flow simulations and network loss calculations at distribution scale.
Make design decisions on how topology data is modeled, stored, and queried efficiently.
Design and enforce secure, compliant data access frameworks for sensitive utility data — including role-based access controls, audit logging, data encryption at rest and in transit, and tenant isolation patterns appropriate for enterprise utility clients in regulated markets.
Architect backend systems using event-driven patterns and message queues to handle asynchronous processing of large-scale batch forecasting jobs, automated reporting workflows, and cross-service coordination — with clear opinions on when synchronous vs. asynchronous patterns are appropriate.
Own database architecture decisions: Cloud SQL (PostgreSQL) for relational data, appropriate NoSQL or time-series stores for high-throughput ingestion, and caching layers.
Design query patterns and indexing strategies that maintain performance as data volumes scale by orders of magnitude across utility clients.
Own the platform infrastructure strategy end-to-end on Google Cloud Platform — defining deployment architecture, environment management, networking, and security posture for a multi-tenant utility-serving platform.
Design and build CI/CD pipelines that reliably deploy data-intensive services, ML-backed APIs, and frontend applications — with appropriate staging, canary deployments, and rollback capabilities.
Define and implement the production observability strategy using Prometheus, Grafana, and GCP Cloud Monitoring — structured logging standards, metrics dashboards, SLO/SLI definitions, and alerting policies to detect and debug issues across data pipelines and forecasting services before they impact utility clients.
Own production reliability and incident response processes for backend systems that utilities rely on for operational planning.
Design for high availability, graceful degradation, and disaster recovery.
Architect containerization and orchestration patterns using Docker that support multi-tenant utility environments with client-specific configurations, data isolation, and independent scaling.
Design deployment patterns and infrastructure automation that allow the platform to scale from 5 utility clients to 50+ without requiring proportional growth in operational overhead — building for self-service onboarding, automated provisioning, and configuration management.
Continuously evaluate and improve system scalability, reliability, cost efficiency, and maintainability — making tradeoff decisions that balance engineering investment against commercial timelines.