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Matter Intelligence

Founding Software Engineer, AI Applications - TrueMeter

Matter Intelligence, San Francisco, California, United States, 94199

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Location

San Francisco Bay Area Employment Type

Full time Location Type

Hybrid Department

TrueMeter Location:

In Person — 3 days a week in San Francisco, CA Team:

Engineering Experience:

2–3 years at an AI-first company or building AI-powered products About TrueMeter

At

TrueMeter , we’re tackling one of the hardest and most overlooked challenges in energy: making sense of

complex, utility data and actually optimizing costs down . Our mission is to

engineer intelligence at scale . We apply

AI-first thinking

to problems where traditional engineering can’t keep up: parsing messy documents, orchestrating agent workflows, and optimizing energy spend across dynamic, multi-variable rate structures. We’re building a

founding team of problem-first engineers

who care deeply about

deploying AI applications into production —fast, reliably, and at scale. Why Join Us?

As our

Founding Software Engineer, AI Applications , you will: Be one of the earliest engineers, setting both the technical direction and cultural foundation of TrueMeter. Own

end-to-end AI applications : from ingesting unstructured data to deploying production-ready agent workflows. Work on

hard, technical problems

that have resisted automation—optimizing rates, parsing unstandardized tariffs, integrating across APIs, and scaling systems that deliver direct business value. Move fast and apply

AI-first

thinking for scaling : leveraging LLMs, orchestration frameworks, and modern cloud infrastructure to design solutions that scale where traditional methods fail. If you want to be on the frontlines of

applying AI to complex, high-impact engineering challenges , this is the role. What You’ll Do

Build AI Applications:

Design and deploy intelligent systems that parse tariffs, optimize utility spend, and automate workflows. Productionize Agent Workflows:

Integrate cutting-edge AI models into robust pipelines that run reliably in real-world environments. Full-Stack Development:

Build APIs, backend services, and frontend integrations using

Python

and

TypeScript

as needed. Leverage Cloud at Scale:

Deploy and maintain systems on

GCP (or AWS) , ensuring scalability, reliability, and performance. Iterate Rapidly:

Prototype, test, and launch features quickly while maintaining production quality. Shape Foundations:

Establish engineering standards, architecture principles, and AI-first practices for the company. What We’re Looking For

2–3 years of experience as a

software engineer

at an AI-first company or building AI-powered applications. Proficiency in

Python

and

TypeScript , with experience shipping production code. Hands-on experience deploying

AI/LLM workflows

into production (LangChain, LlamaIndex, vector DBs, orchestration frameworks, etc.). Familiarity with

GCP (or AWS)

cloud infrastructure and deploying production-grade systems. Experience building and maintaining APIs, data pipelines, or full-stack applications. Startup DNA: thrives in ambiguity, biased toward action, problem-first mindset, and high ownership. Nice-to-Haves

Experience with parsing unstructured data, optimization algorithms, or time-series forecasting. Background in energy, utilities, or IoT data (not required, but valuable context). Prior experience in a

founding or early-stage engineering role . Our Culture

We value

problem-first engineering : we start with the complexity of the problem, and we solve it with the right tools, applying

AI-first thinking

where it creates leverage. We move with urgency, ship quickly, and reflect honestly to improve. We believe that

AI is not an add-on—it’s the core of how we scale solutions to problems that were previously unsolvable. What You’ll Get

A

founding engineer role

with outsized ownership and influence on product and company trajectory. Competitive salary and meaningful equity. Hybrid work culture in

San Francisco —collaborative, fast-paced, and ambitious. The opportunity to work on

real-world hard problems

that push the boundaries of AI-first engineering.

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