> Markdown version of [/jobs/ext/4694810-backend-engineer-models](https://www.wearedevelopers.com/jobs/ext/4694810-backend-engineer-models). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Backend Engineer, Models - **Company:** Meter, Inc. - **Location:** San Francisco, CA, United States - **Salary:** $160,000 - $280,000 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Big Data, Cloud Computing, Complex Networks, Databases, Data Infrastructure, Distributed Systems, Firmware, Machine Learning, Network Connections, Network Administration, Operational Data Store, Data Streaming, Backend, Real-time Inference, Build Tools, Data Pipelines - **Published:** November 12, 2026 - **Apply:** https://www.careerbuilder.com/job-details/backend-engineer-models-san-francisco-ca--cf2958b6-8ccb-4af9-9859-98a2ffa1a6b0 ## About the Role Network Administration/Management, Network Connectivity, Computer Firmware, Wireless Communications, Cloud Computing, Identify Issues, Artificial Intelligence (AI), Production Systems, Data Entry, Application Programming Interface (API), Experience building infrastructure for ML training, evaluation, inference, or experimentation is valuable, but not required. Deep networking expertise is also not a prerequisite. Who Thrives at Meter * Engineers who have demonstrated exceptional ability through the systems they've built, the problems they've solved, and the ownership they've taken. * People who are intellectually curious, technically ambitious, and motivated by the opportunity to build something that hasn't been done before. They move comfortably between architecture and implementation, challenge assumptions, and care deeply about the quality and impact of their work. * Engineers willing to work beyond the boundaries of a traditional job description, learn unfamiliar technologies, and collaborate across disciplines to solve difficult problems. At Meter, you'll have the autonomy to make meaningful technical decisions, work directly with exceptional engineers, and see your work translate into real-world infrastructure operating at scale. ## Description We're looking for a Backend Engineer to help build the foundational infrastructure powering Meter's AI Models team. Our team is developing models that can reason about real-world networks, identify anomalies, diagnose failures, and take action through software. Making this possible requires more than model development. It requires robust systems that can transform massive volumes of real-world network data into reliable inputs for training, evaluation, and inference. You'll build the backend systems and data infrastructure that connect Meter's physical networks to our AI models. This includes designing scalable data pipelines, building APIs and services, developing tools for model experimentation, and creating the infrastructure that allows research to become production intelligence. This is a foundational engineering role with significant ownership and direct access to Meter's founders.. You'll work closely with model engineers, researchers, and engineers across Meter's hardware and software stack to build systems that don't yet exist. What You'll Do * Build foundational data infrastructure: Design and develop scalable systems for ingesting, processing, storing, and accessing massive volumes of network telemetry, configurations, events, and other operational data. Work across distributed systems, streaming data pipelines, and time-series databases to make complex network data accessible and actionable. * Engineer for production scale: Architect and operate reliable, high-performance backend systems and cloud infrastructure capable of supporting large-scale data processing, model development, and real-time inference as Meter's network footprint expands. * Develop backend services and APIs: Build the interfaces that connect production network data with model training, evaluation, experimentation, and inference workflows. * Enable model development: Create reliable data pipelines, datasets, and tooling that allow model engineers to iterate quickly, run experiments, and evaluate model performance against real-world network behavior. * Connect research to real-world systems: Partner with model engineers and software teams to integrate AI capabilities into production infrastructure, enabling models to reason about and eventually act on live networks. * Own problems end to end: Drive technical decisions from architecture and implementation through deployment, monitoring, and continuous improvement.