Founding Engineer (Data Engineer / ML/AI)

Employment Type: Full-time (Contract-to-Hire setup)

Work Model: Remote

Work Hours: US Business Hours alignment

Reports To: Chief Technology Officer (CTO)

Role Overview

We are seeking product-minded, high-agency Founding Engineers across three technical tracks—Software Engineering, Data Engineering, and Data Science / ML Engineering—to architect, build, and scale our circular economy appraisal platform. Our mission is to build the intelligence standard for measuring the residual value of real electronic assets and electronic waste, creating a sustainable end-of-life framework for every device on Earth.

Reporting directly to the CTO, this is a high-ownership architectural seat, not a ticket-executing role. You will own systems end-to-end—from initial design documentation and API/data engineering through containerized cloud deployments, instrumentation, and production monitoring. Operating under a "prepare first, ship fast" culture, you will leverage modern AI tooling (Cursor, Claude Code, Codex) while holding ultimate accountability for code quality, data accuracy, and environmental integrity.

Specialization Tracks & Core Responsibilities

Track 1: Software Engineer

Backend & API Architecture: Design, build, and operate scalable backend services, RESTful/async APIs, and frontend interfaces that power our appraisal engine.
Full Lifecycle Ownership: Own services from technical design documents through containerized cloud deployment (CI/CD), observability, and production maintenance.

Track 2: Data Engineer

Pipeline Infrastructure: Architect, build, and maintain robust ETL/ELT data pipelines, event-driven architectures, and data quality frameworks.
Data Asset Modeling: Transform raw market signals and asset data into trusted, analytics-ready assets, treating data hygiene as a first-class engineering priority.

Track 3: Data Scientist / ML Engineer

Appraisal Modeling: Develop, evaluate, and productionize statistical and machine learning models driving real-time asset valuation and solar asset appraisals.
Applied Analytics: Bridge statistical rigor with real-world financial and environmental outcomes, creating explainable valuation frameworks.

Universal Founding Responsibilities

End-to-End System Ownership: Define system health metrics, instrument observability logging/alerting before shipping, and maintain what you build in cloud environments.
Design & Documentation Culture: Author design documents for consequential architectural decisions prior to writing code; maintain technical documentation as part of the strict definition of done.
Quality Assurance & AI Leverage: Aggressively utilize AI tools to accelerate build speed while taking 100% personal ownership of every line of code deployed.
Early Escalation & Transparency: Surface technical blockers, production anomalies, and architectural issues immediately to prevent cascading failures.

Required Skills & Qualifications

Professional Track Record: 3–5+ years of professional engineering experience shipping and operating production systems in cloud environments (AWS/GCP/Azure).
Core Technical Stack: Advanced proficiency in Python (production-quality code architectures) and/or track-relevant languages (TypeScript, SQL).
Database & SQL Mastery: Advanced SQL fundamentals, including schema design, complex query optimization, relational database modeling, and performance intuition.
US Work Authorization: Must be legally authorized to work in the United States without current or future visa sponsorship requirements.
Engineering Mindset: High agency, strong opinions weakly held, commitment to writing design docs before executing, and a customer-first focus.

Track-Specific & Preferred Qualifications

Software Engineering Track: Experience with async programming, relational database optimization, containerized deployments (Docker/Kubernetes), and automated CI/CD pipelines.
Data Engineering Track: Experience with data modeling, orchestration tools (Airflow, Dagster, Prefect), data quality frameworks (Great Expectations, dbt), and event-driven data streaming.
Data Science / ML Track: Proven track record productionizing ML models, experimental design, and building applied analytics frameworks that drive financial decisions.
Infrastructure & Security: Infrastructure-as-Code (Terraform) and security fundamentals (IAM, secrets management).

Success Metrics (KPIs)

Production Reliability & System Health: Zero unmonitored production outages or silent data pipeline failures.
Delivery Velocity & Preparation: Consistently moving from approved technical design documents to production-ready releases within sprint targets.
Data Integrity & Valuation Accuracy: Delivering reliable, audit-ready asset valuations that customers and partners can trust for financial decisions.

Founding Engineer - 21172