About the job
CVector is dedicated to revolutionizing economic optimization and AI-driven predictions across energy and manufacturing sectors.
Our technology seamlessly integrates critical decision-making factors that impact cost, reliability, and profit margins, providing a unified decision layer that forecasts future scenarios, simulates potential outcomes, and optimizes operational strategies. This allows industrial plants to operate closer to their maximum economic potential daily.
This position requires in-office attendance at our New York City headquarters four days a week. CVector serves clients nationwide and operates in challenging industrial environments.
Role Overview
As a Senior Software Engineer specializing in Backend and AI Infrastructure, you will be instrumental in advancing CVector’s backend platform. Your focus will be on developing time-series data systems, AI-enhanced analytics, cloud infrastructure, and data ingestion pipelines that underpin our client-facing applications and internal modeling tools.
This role is ideal for engineers who thrive on working closely with data and infrastructure, possess excellent architectural judgment, and are eager to engage with AI systems, databases, and distributed backend services. You will take ownership of complex systems, lead significant technical migrations, and influence the integration of intelligence into industrial energy workflows.
You will work in close collaboration with product, modeling, and frontend engineers, significantly impacting the platform's direction, reliability, and scalability for the long term.
Key Responsibilities
As a Senior Software Engineer, your contributions will span various interconnected domains:
Intelligent Systems
- Translate customer domains and operational workflows into effective prompts and AI system interfaces.
- Design, implement, and refine evaluations for AI outputs.
- Incorporate customer feedback and reinforcement signals to enhance system performance.
- Optimize context selection, retrieval, and trace collection to elevate output quality.
- Fine-tune smaller models using collected traces to enhance speed while maintaining performance standards.
- Evaluate and assimilate new AI platforms and models as they emerge.
- Support the training and deployment of large, time-series-specific models.
Backend Platform and Data Infrastructure
- Oversee migrations and enhancements of our time-series data schemas and storage systems.
- Update and manage PostgreSQL and associated database infrastructure.
- Develop and sustain data connectors for industrial and external systems.
- Lead improvements to MQTT-based data ingestion pipelines.
- Transition PostgREST to a GraphQL-based framework and evolve our API architecture.

