Rapid Prototyping Engineer (Software)

Mine Vision Systems•Pittsburgh, PA

3d•Hybrid

About The Position

Mine Vision Systems is a mining technology company building the decision-making platform for underground mining. We introduce high-fidelity data into the mining workflow that has simply never existed before, enabling operators to move from assumptions to evidence-based decisions. Our digital infrastructure is designed to map, monitor, and manage the underground mining environment with confidence, creating a persistent, data-rich foundation for operational and strategic decision-making. Since the launch of our flagship product, FaceCapture, in late 2023, adoption has accelerated rapidly as customers realize the value of truly understanding their underground operations. We focus on underground mining of critical minerals and precious metals, where small improvements in accuracy and insight drive outsized returns. Our technology delivers millions of dollars in annual value by minimizing overbreak, improving ore calls, enhancing resource models, and enabling a growing set of high-impact applications across the mine lifecycle. By transforming raw underground data into actionable intelligence, Mine Vision Systems empowers mining teams to operate more efficiently, more predictably, and with greater confidence than ever before. Role Summary We are looking for an AI-forward Rapid Prototyping Engineer who can turn an idea into a working customer-facing prototype in days, and then turn that prototype into a first-class contribution to our production codebase. You will pair with our product team and sales team and sometimes participate in customer and prospect meetings, listening to what’s actually being asked for, and prototyping it fast with AI as your daily multiplier, and you will close the loop by refactoring those experiments into clean, maintainable production code that meets our engineering bar. This role is build-heavy, customer-facing, and AI-forward; it prioritizes real software-engineering depth, the judgment to fix what AI subtly gets wrong, and the personal range to be comfortable in a sales meeting one day and in a code review the next. The right person treats a prototype as a starting point for production, not a separate world — and the right person is someone the sales team is glad to bring into a customer room.

Requirements

3+ years of professional software engineering, real production code, not bootcamp or hobby projects.
Strong in Python or C++, ideally both. Rust is a plus. You can read, write, and refactor in at least one deeply.
Demonstrable hands-on use of AI tools (Claude Code CLI, Codex, etc.) as a daily multiplier in your own work. You will be expected to show this directly in the interview process.
Comfortable reading and refactoring code you didn’t write, including older code with imperfect documentation.
Personable and customer-comfortable, able to answer a customer’s technical question on the spot, comfortable being respectfully wrong out loud, and able to read a room.
Strong written communication, you can summarize what you built and why, in a way that can be understood by product, sales, engineering, and customers.
Bias toward shipping working things vs just talk; bias toward putting prototypes into the real codebase vs rotting in a sandbox.

Nice To Haves

Background in mining, robotics, embedded, or industrial technology.
Familiarity with our stack, Python and C++ services, web-app frontends, AWS, and container workflows.
Prior experience in a Forward Deployed Engineer / Solutions Engineer / Pre-Sales Engineer / customer-embedded role, or as a startup founding engineer, where customer time and engineering time happened on the same day.

Responsibilities

Rapid prototyping with AI: Build working prototypes of customer-requested capabilities in hours-to-days, using AI tools as a daily multiplier; iterate fast and validate with real users.
Customer and sales partnership: Join sales calls and customer prospect meetings as a technical voice, listen to the real underlying need, demonstrate options live, and translate ambiguous customer language into concrete prototypes.
Cross-functional bridge: Pair with Product to align prototypes with the roadmap; pair with the rest of the software team to land prototypes cleanly in the codebase rather than over the wall.
Prototype → production refactoring: Carry your prototypes into the production codebase with the support of the engineering team; respect existing architecture, match the codebase’s lints, types, tests, and directory conventions, and meet the team’s quality bar.
Production code contribution: Contribute regular features and improvements to the production codebase between prototype cycles. This is not a sandbox-only role.
AI-assisted code authoring with judgment: Read and validate AI-generated code; identify and fix the subtle errors AI gets wrong; treat AI output as a starting point, not an answer.
Live demos: Build demo-quality artifacts that survive scrutiny in a real customer room — clean enough to ship, real enough to use, not slideware.
Internal sharing and recognition: Document prototypes, post short demos in shared channels, and propose follow-on work where a prototype clearly hits.