Quantum Model Platform Engineer

PassiveLogic•Holladay, UT

2d•Onsite

About The Position

This is a career-defining opportunity to play a crucial role in a hyper-scale AI company that is transforming the future of autonomous systems, energy, and the built environment. As a Quantum Model Platform Engineer, you will own the systems that turn PassiveLogic's Quantum models - graph-based digital representations of building equipment and systems - into validated, published assets that teams can trust. You will develop the workflows that validate and promote models, build largely web-based tools that explain what is deployed and where it has drifted, and investigate issues across model data, APIs, automation, and user interfaces. You will work across JavaScript, Python, and Swift. Prior Swift experience is not required. What matters is a strong software foundation, demonstrated success learning unfamiliar technologies, and the curiosity to understand a complex system by reading, running, and reasoning about its code.

Requirements

Proven software engineering experience: A track record of delivering and maintaining software that other people rely on, including diagnosing failures and improving systems over time.
Web application fundamentals: Experience creating useful HTML interfaces with CSS and JavaScript, and connecting those interfaces to APIs or structured datasets.
Scripting and automation experience: Ability to use Python or a similar scripting language to transform data, automate repeatable work, and build maintainable operational tools.
Relational data experience: Practical experience working with relational databases, including schemas, queries, relationships, and data-integrity concerns.
Code-centered systems thinking: A strong desire to understand how a complex system actually works, with the ability to follow behavior through unfamiliar code, data, tests, logs, and service boundaries.
Ability to learn new languages and frameworks: You do not need prior Swift experience, but you must be able to show how you have become productive in an unfamiliar language, framework, or codebase.
A thoughtful AI-assisted development workflow: Ability to demonstrate how you use AI tools to explore systems, plan and implement changes, and accelerate your work while verifying results through source inspection, tests, reviews, and sound engineering judgment.
Ownership and communication: Ability to work independently, explain technical findings clearly, ask precise questions, and collaborate with engineers and domain experts across teams.

Nice To Haves

Experience with software delivery workflows: Familiarity with Git, code review, automated testing, CI/CD pipelines, release processes, and maintaining production or internal developer tools.
Experience with APIs and structured data: Comfort working with technologies such as GraphQL, REST, WebSockets, JSON, and YAML, including debugging authentication, pagination, concurrency, and schema changes.
Data-focused interface experience: Experience presenting complex or high-volume data through search, filtering, comparison views, visualizations, or operational dashboards.
Strong troubleshooting habits: Ability to use logs, reproducible examples, tests, and direct code inspection to separate symptoms from root causes in systems with multiple moving parts.
Pragmatic product judgment: Ability to turn ambiguous user needs into focused tools and workflows that are understandable, maintainable, and useful in day-to-day operations.
Swift experience: Familiarity with Swift, Swift Package Manager, command-line applications, asynchronous programming, or the macOS development ecosystem.
Graph data experience: Experience with graph databases, graph data structures, graph queries, graph comparison algorithms, or systems where identity and relationships are central to correctness.
Digital twin or ontology experience: Familiarity with digital twins, semantic models, ontologies, model validation, or schema-driven systems.
Building systems knowledge: Exposure to building automation, HVAC equipment, controls, commissioning, or operational building data.
Data and deployment tooling: Experience with GraphQL clients, static web applications, GitLab CI/CD, artifact-based workflows, data visualization libraries, or environment-to-environment promotion.

Responsibilities

Own Quantum model validation and publishing: Maintain and improve the workflows that validate models, manage their release state, and publish approved models reliably across environments.
Build tools that make deployed models understandable: Create and evolve web applications, dashboards, reports, and visualizations that help teams inspect model libraries, compare environments, identify drift, and understand deployed building systems.
Develop automation across the toolchain: Write and maintain JavaScript applications, Python scripts, and Swift command-line tools that export, import, compare, audit, and reconcile model data.
Investigate behavior end to end: Trace failures and unexpected results across source models, relational and graph-shaped data, GraphQL and other APIs, asynchronous jobs, CI/CD pipelines, and browser interfaces until the underlying cause is understood.
Protect model quality and data integrity: Design tests, validation checks, deployment safeguards, and clear operational reports that prevent invalid, untested, duplicated, or inconsistent models from reaching downstream teams.
Partner with model authors and platform teams: Turn domain and engineering needs into practical tools, document how the systems work, communicate risks clearly, and leave workflows easier for the next person to understand and operate.