CVector’s mission is to bring real time economic optimization and AI prediction to every energy and manufacturing plant.Industrial facilities make decisions every minute that determine cost, reliability, and margin, but the signals that matter live in different worlds: live asset constraints and process reality on one side, feedstock prices, product prices, demand, and market dynamics on the other. We fuse those worlds into one decision layer that continuously forecasts what is coming, simulates what could happen, and optimizes what to do next, so plants can run closer to their true economic potential every day.This position works from our New York City office four days per week. CVector has customers across the United States and operates real-world systems in demanding industrial environments.Role OverviewAs a Senior Software Engineer - Backend & AI Infra focus, you will play a critical role in evolving CVector’s core backend platform. You will work on time-series data systems, AI-assisted analytics, cloud infrastructure, and data ingestion pipelines that power our customer-facing applications and internal modeling platforms.This role is well-suited for an engineer who enjoys working close to the data and infrastructure layers, has strong architectural judgment, and is excited to operate across AI systems, databases, and distributed backend services. You will take ownership of complex systems, drive major technical migrations, and help shape how intelligence is embedded into industrial energy workflows.You will collaborate closely with product, modeling, and frontend engineers, and you will have significant influence over platform direction, reliability, and long-term scalability.Key ResponsibilitiesAs a Senior Software Engineer, you will contribute across several interconnected areas:Intelligent SystemsMap customer domains and operational workflows into effective prompts and AI system interfacesDesign, execute, and iterate on evals for AI outputsIncorporate customer feedback and reinforcement signals to improve system behaviorRefine context selection, retrieval, and trace collection to improve output qualityFine-tune smaller models using collected traces to reduce latency while preserving performanceEvaluate and integrate new AI platforms and models as they become availableSupport training and deployment of large, time-series-focused modelsBackend Platform & Data InfrastructureLead migrations and upgrades of our time-series data schemas and storage enginesUpgrade and maintain PostgreSQL and related database infrastructureDevelop and maintain data connectors for industrial and third-party systemsLead MQTT-based data ingestion pipeline improvementsTransition PostgREST to GraphQL-based framework and evolve our API architectureImprove TigerData to next-gen time series design and simplify multi-tenant provisioning workflowsDatabase & Analytics SystemsOptimize performance and reliability of high-volume time-series data storesDesign and execute architecture plans for TigerDataAugment analytical workloads using Parquet and/or Iceberg-based storage formatsBalance real-time and historical query performance across operational and analytical use casesModeling Platform SupportImprove and consolidate internal machine learning systemsEnable parallelized and distributed model training workflowsImplement message brokers and orchestration mechanisms for multi-stage learning pipelinesImprove reproducibility, traceability, and coordination across modeling stagesReliability & Developer ExperienceStrengthen cloud infrastructure uptime, observability, and deployment reliabilityStandardize build, test, and release processes across servicesImprove developer tooling and internal platform ergonomicsPort backend services from bun to Node.js where appropriateTrack and improve DORA metrics (deployment frequency, lead time, change failure rate, recovery time)Participate in an on-call rotation and continuously improve operational readinessRequirementsOur current technology stack includes Python, TypeScript, Supabase, PostgreSQL, MQTT, TigerData, InfluxDB, AWS, and GitHub-based CI/CD workflows.A strong candidate will bring:A relevant engineering degree or equivalent professional experience with a strong computer science foundation2+ years experience building and operating production backend systemsStrong proficiency in Python and/or TypeScript for backend developmentExperience with databases, especially PostgreSQL and time-series or analytical data storesFamiliarity with event-driven systems, streaming data, or message brokersExperience designing or supporting AI/ML systems in production is a strong plusComfort working on infrastructure, data pipelines, and evolving system architecturesStrong communication skills and the ability to collaborate in a high-ownership environmentExperience working in a startup or fast-moving product organizationWillingness to travel occasionally to customer sites to understand real-world constraintsWorking experience in FinTech, quantitative analysis, and econometrics greatly preferredBenefitsCVector provides team members with:A competitive compensation package with meaningful equity upsideA robust selection of health, dental, and vision insurance optionsOptional Health Flexible Spending Account (FSA)Unlimited PTO with a three week minimum per year, plus additional sick daysTop of the line work equipment and IT setup, including high performance laptops and a modern developer stackUnlimited access to the latest AI agent systems and productivity tools to help you move faster and build betterVisa support for candidates currently based in the US, including employer sponsored visa applications where applicable The anticipated base salary range for this role is $132,000 to $150,000.Why This Role Matters at CVectorThis role sits at the intersection of AI systems, time-series data, and real-world energy infrastructure. Your work will directly impact system reliability, customer trust, and CVector’s ability to scale intelligent energy management across industries.If you enjoy solving deep backend problems, shaping platform foundations, and embedding intelligence into physical systems, this role offers both technical challenge and real-world impact.
CVector’s mission is to bring real time economic optimization and AI prediction to every energy and manufacturing plant.
Industrial facilities make decisions every minute that determine cost, reliability, and margin, but the signals that matter live in different worlds: live asset constraints and process reality on one side, feedstock prices, product prices, demand, and market dynamics on the other. We fuse those worlds into one decision layer that continuously forecasts what is coming, simulates what could happen, and optimizes what to do next, so plants can run closer to their true economic potential every day.
This position works from our New York City office four days per week. CVector has customers across the United States and operates real-world systems in demanding industrial environments.
Role Overview
As a Senior Software Engineer - Backend & AI Infra focus, you will play a critical role in evolving CVector’s core backend platform. You will work on time-series data systems, AI-assisted analytics, cloud infrastructure, and data ingestion pipelines that power our customer-facing applications and internal modeling platforms.
This role is well-suited for an engineer who enjoys working close to the data and infrastructure layers, has strong architectural judgment, and is excited to operate across AI systems, databases, and distributed backend services. You will take ownership of complex systems, drive major technical migrations, and help shape how intelligence is embedded into industrial energy workflows.
You will collaborate closely with product, modeling, and frontend engineers, and you will have significant influence over platform direction, reliability, and long-term scalability.
Key Responsibilities
As a Senior Software Engineer, you will contribute across several interconnected areas:
Intelligent Systems
- Map customer domains and operational workflows into effective prompts and AI system interfaces
- Design, execute, and iterate on evals for AI outputs
- Incorporate customer feedback and reinforcement signals to improve system behavior
- Refine context selection, retrieval, and trace collection to improve output quality
- Fine-tune smaller models using collected traces to reduce latency while preserving performance
- Evaluate and integrate new AI platforms and models as they become available
- Support training and deployment of large, time-series-focused models
Backend Platform & Data Infrastructure
- Lead migrations and upgrades of our time-series data schemas and storage engines
- Upgrade and maintain PostgreSQL and related database infrastructure
- Develop and maintain data connectors for industrial and third-party systems
- Lead MQTT-based data ingestion pipeline improvements
- Transition PostgREST to GraphQL-based framework and evolve our API architecture
- Improve TigerData to next-gen time series design and simplify multi-tenant provisioning workflows
Database & Analytics Systems
- Optimize performance and reliability of high-volume time-series data stores
- Design and execute architecture plans for TigerData
- Augment analytical workloads using Parquet and/or Iceberg-based storage formats
- Balance real-time and historical query performance across operational and analytical use cases
Modeling Platform Support
- Improve and consolidate internal machine learning systems
- Enable parallelized and distributed model training workflows
- Implement message brokers and orchestration mechanisms for multi-stage learning pipelines
- Improve reproducibility, traceability, and coordination across modeling stages
Reliability & Developer Experience
- Strengthen cloud infrastructure uptime, observability, and deployment reliability
- Standardize build, test, and release processes across services
- Improve developer tooling and internal platform ergonomics
- Port backend services from bun to Node.js where appropriate
- Track and improve DORA metrics (deployment frequency, lead time, change failure rate, recovery time)
- Participate in an on-call rotation and continuously improve operational readiness
Requirements
Our current technology stack includes Python, TypeScript, Supabase, PostgreSQL, MQTT, TigerData, InfluxDB, AWS, and GitHub-based CI/CD workflows.
A strong candidate will bring:
- A relevant engineering degree or equivalent professional experience with a strong computer science foundation
- 2+ years experience building and operating production backend systems
- Strong proficiency in Python and/or TypeScript for backend development
- Experience with databases, especially PostgreSQL and time-series or analytical data stores
- Familiarity with event-driven systems, streaming data, or message brokers
- Experience designing or supporting AI/ML systems in production is a strong plus
- Comfort working on infrastructure, data pipelines, and evolving system architectures
- Strong communication skills and the ability to collaborate in a high-ownership environment
- Experience working in a startup or fast-moving product organization
- Willingness to travel occasionally to customer sites to understand real-world constraints
- Working experience in FinTech, quantitative analysis, and econometrics greatly preferred
Benefits
CVector provides team members with:
- A competitive compensation package with meaningful equity upside
- A robust selection of health, dental, and vision insurance options
- Optional Health Flexible Spending Account (FSA)
- Unlimited PTO with a three week minimum per year, plus additional sick days
- Top of the line work equipment and IT setup, including high performance laptops and a modern developer stack
- Unlimited access to the latest AI agent systems and productivity tools to help you move faster and build better
- Visa support for candidates currently based in the US, including employer sponsored visa applications where applicable
The anticipated base salary range for this role is $132,000 to $150,000.
Why This Role Matters at CVector
This role sits at the intersection of AI systems, time-series data, and real-world energy infrastructure. Your work will directly impact system reliability, customer trust, and CVector’s ability to scale intelligent energy management across industries.
If you enjoy solving deep backend problems, shaping platform foundations, and embedding intelligence into physical systems, this role offers both technical challenge and real-world impact.
We built CVector to help create an affordable, resilient, and safe global infrastructure that runs nearly autonomously. To achieve that, CVector aims to put a brain and a nervous system into every energy generating, storing, and consuming asset in the industrial world.
We built CVector to help create an affordable, resilient, and safe global infrastructure that runs nearly autonomously. To achieve that, CVector aims to put a brain and a nervous system into every energy generating, storing, and consuming asset in the industrial world.
