Rapid Prototyping Engineer (Software)

📍 Job Overview

Job Title: Rapid Prototyping Engineer (Software)

Company: Mine Vision Systems

Location: Pittsburgh, Pennsylvania, United States

Job Type: FULL_TIME

Category: Software Engineering / GTM Engineering

Date Posted: 2026-06-01

Experience Level: Mid-Level (3-5 years)

Remote Status: Hybrid

🚀 Role Summary

• This role focuses on rapidly transforming customer-driven concepts into functional, customer-facing software prototypes using AI as a primary development accelerator.

• It requires a blend of deep software engineering expertise and the ability to effectively communicate technical solutions in customer and sales environments, acting as a crucial bridge between technical development and market needs.

• Candidates will be responsible for not only building prototypes but also for seamlessly integrating them into the production codebase, ensuring high-quality, maintainable software.

• The position emphasizes a "build-heavy" approach where prototypes are viewed as the starting point for production-ready features, demanding rigorous engineering standards and a proactive stance in contributing to the core product.

📝 Enhancement Note: This role is classified under Software Engineering with a strong GTM (Go-To-Market) Engineering component due to the direct involvement with sales and customer interactions for prototype development. The emphasis on AI as a "daily multiplier" and the rapid prototyping cycle suggest a fast-paced, innovative environment. The need to refactor prototypes into production code necessitates strong software architecture and clean code principles.

📈 Primary Responsibilities

• Rapid Prototyping with AI: Develop functional software prototypes for customer-requested features within days, leveraging AI tools (e.g., Claude Code CLI, Codex) to accelerate development and iterate quickly based on user feedback.

• Customer & Sales Partnership: Actively participate in sales calls and customer meetings, serving as the technical authority to understand underlying customer needs, demonstrate potential solutions live, and translate ambiguous requirements into concrete, actionable prototype designs.

• Cross-Functional Integration: Collaborate closely with the Product team to ensure prototypes align with the long-term product roadmap and with the broader software engineering team to facilitate the smooth integration of prototypes into the main codebase, avoiding "over-the-wall" handoffs.

• Prototype-to-Production Refactoring: Champion the transition of validated prototypes into the production codebase, adhering to existing architectural patterns, coding standards, and quality benchmarks, with support from the wider engineering team.

• Production Code Contribution: Contribute regular features, bug fixes, and enhancements to the core production software between prototyping cycles, demonstrating commitment to the product's ongoing development beyond experimental phases.

• AI-Assisted Development & Validation: Critically review, validate, and refine AI-generated code, identifying and correcting subtle errors to ensure functionality, performance, and adherence to engineering best practices.

• Live Demonstration Delivery: Create and present robust, demo-quality artifacts that can withstand scrutiny in customer-facing scenarios, ensuring they are clean enough to ship and functional enough for immediate use, not just conceptual mockups.

• Knowledge Sharing & Internal Advocacy: Document prototypes, share short video demonstrations through internal channels, and proactively propose follow-on development for successful prototypes that demonstrate significant customer value.

📝 Enhancement Note: The responsibilities highlight a unique hybrid role that bridges the gap between innovative R&D (rapid prototyping with AI) and direct customer engagement (sales calls, demos), culminating in production code contribution. The emphasis on "shipping working things" and "putting prototypes into the real codebase" indicates a strong bias towards tangible delivery and product integration.

🎓 Skills & Qualifications

Education: While no specific degree is mandated, a strong foundation in Computer Science or a related field is implicitly expected given the professional experience requirement.

Experience:

Required Skills:

• Programming Proficiency: Strong command of Python or C++, with Rust being a significant plus. The ability to read, write, and refactor code deeply in at least one of these languages is essential.

• AI Tool Utilization: Demonstrable, hands-on experience using AI tools (e.g., Claude Code CLI, Codex) as a daily multiplier in software development workflows. Candidates will be expected to showcase this directly during the interview process.

• Code Comprehension & Refactoring: Proven ability to read, understand, and refactor existing codebases, including legacy code with potentially limited documentation.

• Customer Interaction Aptitude: Personable and comfortable engaging with customers, capable of answering technical questions accurately, acknowledging when unsure or incorrect respectfully, and adept at reading social cues in a room.

• Technical Communication: Strong written communication skills, enabling the clear summarization of built features, their rationale, and impact for diverse audiences including Product, Sales, Engineering, and external customers.

• Delivery Bias: A clear preference for shipping working software and integrating prototypes into the main codebase rather than keeping them confined to sandboxed environments.

Preferred Skills:

• Background in mining technology, robotics, embedded systems, or industrial technology.

• Familiarity with Mine Vision Systems' technology stack, including Python and C++ services, web-app frontends, AWS, and containerization workflows (e.g., Docker, Kubernetes).

• Previous experience in roles such as Forward Deployed Engineer, Solutions Engineer, Pre-Sales Engineer, or a customer-embedded technical role, where customer interaction and engineering tasks often occurred concurrently.

• Experience as a startup founding engineer, demonstrating adaptability and broad technical ownership.

📝 Enhancement Note: The requirement for "real production code, not bootcamp or hobby projects" and the explicit need to demonstrate AI tool usage directly in the interview process are critical indicators of the company's focus on practical, impactful engineering. The preference for specific programming languages and related fields like robotics or mining provides clear technical direction.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Demonstration of Rapid Prototyping: Showcase examples where you quickly developed functional prototypes to address specific user needs or business problems, highlighting the speed-to-delivery.

• AI Integration Examples: Provide case studies or project descriptions demonstrating how you've effectively integrated AI tools into your development workflow to enhance productivity or solve complex problems.

• Production Code Contributions: Include examples of features or significant code contributions you've made to production systems, demonstrating adherence to software quality standards, testing, and architectural best practices.

• Customer-Facing Artifacts: If possible, include examples of technical demonstrations, documentation, or presentations designed for customer or sales audiences, illustrating your ability to translate technical concepts into business value.

• Refactoring and Code Quality: Highlight projects where you've significantly refactored existing code, improved its maintainability, or ensured it meets stringent quality and testing requirements.

Process Documentation:

• Workflow Design & Optimization: Be prepared to discuss your process for translating ambiguous requirements into structured development plans and how you optimize workflows for rapid iteration and efficient delivery, especially when leveraging AI.

• AI Tool Integration Strategy: Articulate your methodology for selecting and integrating AI coding tools into a development lifecycle, including validation, refinement, and integration into production systems.

• Prototype-to-Production Pipeline: Detail your approach to ensuring prototypes are developed with production readiness in mind, covering aspects like code quality, architectural alignment, testing strategies, and collaboration with the core engineering team.

• Performance Measurement: Describe how you measure the success of prototypes and their subsequent integration into production, focusing on metrics related to development speed, customer adoption, and impact on the core product.

📝 Enhancement Note: For a role emphasizing rapid prototyping and AI integration, a portfolio demonstrating practical application is key. Candidates should focus on showcasing their ability to deliver working software quickly, their proficiency with AI tools, and their capacity to ensure these rapid developments transition seamlessly into robust production code.

💵 Compensation & Benefits

Salary Range:

Benefits:

• Competitive compensation package.

• Comprehensive health benefits including Medical, Dental, and Vision insurance.

• Disability Insurance.

• Life Insurance.

• 401(k) retirement plan with employer match, supporting long-term financial planning.

• Uncounted Paid Time Off (PTO) policy, offering flexibility and encouraging work-life balance.

• Opportunity to work with a fast-paced, growing company with tangible hardware products in the field.

Working Hours:

• The role is full-time, with an expected standard of 40 hours per week. However, given the "build-heavy" and customer-facing nature, flexibility may be required to meet project deadlines or customer needs, aligning with the company's hybrid work model.

📝 Enhancement Note: The salary range is estimated based on typical compensation for similar roles in Pittsburgh, PA, considering the tech industry and the specific demands of this hybrid GTM/Engineering position. The provided benefits are directly from the job description, with emphasis added on aspects relevant to operations and engineering professionals.

🎯 Team & Company Context

🏢 Company Culture

Industry: Mining Technology. Mine Vision Systems is at the forefront of providing advanced digital solutions to the mining sector, specifically focusing on underground operations. This involves leveraging high-fidelity data and AI to drive evidence-based decision-making.

Company Size: Small to Medium-sized (Implied by "Small, fast-moving team"). This suggests a dynamic environment where individual contributions have a significant and visible impact.

Founded: The company's flagship product, FaceCapture, launched in late 2023, indicating a relatively young but rapidly growing organization with a strong product-market fit.

Team Structure:

• The team is described as "small" and "fast-moving," suggesting a flatter hierarchy and direct collaboration lines.

• This role requires close pairing with Product and Sales teams, indicating a highly cross-functional operational structure.

Methodology:

• AI-Driven Development: A core methodology is the use of AI as a multiplier for rapid prototyping and code generation.

• Customer-Centric Iteration: Prototypes are built based on direct customer requests and feedback, with rapid iteration cycles.

• Production-Quality Standards: A strong emphasis is placed on ensuring that all developed code, including prototypes, meets rigorous production quality, testing, and architectural standards.

• Evidence-Based Decision Making: The company's overall mission is to enable evidence-based decisions in mining through data, reflecting a data-driven culture internally as well.

Company Website: https://www.minevisionsystems.com/

📝 Enhancement Note: The company operates in a niche but critical industry (mining technology) with a focus on innovation through AI and data. The culture appears to be fast-paced, customer-focused, and engineering-centric, valuing individuals who can bridge technical development with market needs.

📈 Career & Growth Analysis

Operations Career Level: This role represents a specialized mid-level to senior-level engineering position. It offers a unique blend of deep technical development ("real production code") and direct customer/sales engagement. It's not a traditional GTM role nor a pure R&D role but a hybrid that requires significant engineering depth.

Reporting Structure: While not explicitly stated, the role's collaboration with Product and Sales teams suggests reporting into an engineering management structure, with close daily interaction and alignment with Product and Sales leadership.

Operations Impact: The engineer's work directly influences revenue generation by rapidly prototyping features that address customer needs, thereby accelerating sales cycles and product adoption. Successful prototypes become core product features, directly enhancing the company's offering and competitive edge in the mining technology market. The ability to make AI look good and the team look better implies a leadership potential in shaping development practices.

Growth Opportunities:

• Technical Specialization: Deepen expertise in AI-assisted development, rapid prototyping, and potentially specific domains like mining technology or robotics.

• Product Development Leadership: Transition into a role with more influence over product roadmap decisions based on prototype validation and customer feedback.

• Engineering Quality Advocacy: Grow into a position focused on maintaining and elevating the engineering bar for production code, potentially leading code reviews or architectural discussions.

• GTM Engineering Expertise: Develop further as a hybrid engineer who can effectively translate market needs into technical solutions and represent the company technically in customer-facing scenarios.

📝 Enhancement Note: This role offers significant growth potential for engineers who thrive in dynamic, customer-facing environments and want to see their work directly impact product development and sales. The blend of skills required positions candidates for unique career paths within technology companies focused on innovation and market responsiveness.

🌐 Work Environment

Office Type: The description mentions a "flexible hybrid work model," suggesting a blend of remote and in-office work. Specific details on office days are not provided, but the company is based in Pittsburgh, PA.

Office Location(s): Pittsburgh, Pennsylvania, with a postal code of 15206.

Workspace Context:

• Collaborative Environment: The role emphasizes close pairing with Product and Sales teams, indicating a highly collaborative workspace, whether in-person or virtual.

• Technology Access: The company provides the necessary tools and technology for rapid prototyping and production development, including access to AI tools and likely cloud infrastructure (AWS mentioned).

• Team Interaction: Opportunities for direct interaction with a small, fast-moving team, fostering knowledge sharing and quick decision-making.

Work Schedule: Full-time (40 hours/week) with a flexible hybrid work model. This arrangement allows for focused individual work while retaining opportunities for in-person collaboration and team integration.

📝 Enhancement Note: The hybrid model offers a balance between focused, independent work (potentially remote) and collaborative team engagement (in-office). The company's focus on rapid development and customer interaction suggests a dynamic and responsive work environment.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: Likely a review of resume and portfolio to assess foundational engineering skills, Python/C++ proficiency, and AI tool experience.

• Technical Deep Dive: Expect in-depth coding challenges focused on Python or C++, potentially involving AI-assisted code generation and refactoring. This stage will likely include live coding sessions and discussions about past projects.

• AI Tool Demonstration: A critical part of the interview will be demonstrating your practical, hands-on experience with AI coding tools in real-time or through a pre-prepared example.

• Customer/Sales Scenario Simulation: You may be asked to participate in a simulated customer meeting or a role-playing exercise to assess your ability to communicate technical concepts, understand needs, and respond to customer queries.

• Portfolio Review & Discussion: A dedicated session to walk through your portfolio, focusing on examples of rapid prototyping, AI integration, production code contributions, and successful refactoring projects.

• Cultural Fit & Team Collaboration: Discussions to assess your alignment with the company's fast-paced, customer-centric, and collaborative culture.

Portfolio Review Tips:

• Show, Don't Just Tell: Prioritize examples where you can demonstrate working code, live demos, or tangible outcomes.

• Highlight AI Integration: Clearly articulate the specific AI tools used, how they accelerated your work, and how you validated/refined the AI-generated output.

• Emphasize Prototype-to-Production: For any prototype examples, clearly explain the transition process to production code, including challenges faced and how they were overcome.

• Quantify Impact: Where possible, use metrics to demonstrate the speed of your prototyping, the impact of your contributions on customer needs, or the efficiency gains from AI usage.

• Structure Case Studies: For each significant project, present a clear structure: Problem, Your Solution (including AI usage), Implementation details (code quality, refactoring), and Outcome/Impact.

Challenge Preparation:

• Master Core Languages: Be ready for coding challenges in Python and/or C++, including algorithmic problems and practical application scenarios.

• Practice AI Prompting: Familiarize yourself with effective prompting techniques for AI coding assistants to generate functional code snippets and solutions.

• Prepare for Refactoring Tasks: Practice identifying code smells, improving code structure, and optimizing performance in existing code.

• Develop Customer Communication Scenarios: Rehearse how you would explain complex technical ideas to a non-technical audience and how you handle questions about feasibility and timelines.

• Research Mine Vision Systems: Understand their products, target market (underground mining), and the value proposition of their technology.

📝 Enhancement Note: The interview process is heavily geared towards practical demonstration of skills, particularly AI tool proficiency and the ability to translate rapid prototypes into production code. A well-curated portfolio that clearly illustrates these capabilities will be crucial for success.

🛠 Tools & Technology Stack

Primary Tools:

• Programming Languages: Python (primary), C++ (primary), Rust (preferred).

• AI Development Tools: Claude Code CLI, Codex, and similar AI-assisted coding platforms.

• Version Control: Git (standard practice for software engineering).

• Containerization: Docker, Kubernetes (familiarity is desirable).

Analytics & Reporting:

CRM & Automation:

Cloud Platform:

• AWS (Amazon Web Services) is mentioned as a desirable familiarity, suggesting the company's production environment likely leverages cloud infrastructure.

📝 Enhancement Note: The core technical stack revolves around Python and C++ development, heavily augmented by AI coding assistants. Familiarity with cloud environments like AWS and containerization is a plus, reflecting modern software development practices.

👥 Team Culture & Values

Operations Values:

• Speed and Agility: A strong bias towards shipping working software quickly ("build-heavy," "prototypes in days").

• Customer Focus: Directly engaging with customers and sales to understand and meet market needs.

• Engineering Rigor: Commitment to high-quality, maintainable production code, even for features that originated as rapid prototypes.

• AI as a Multiplier: Embracing AI tools not just for convenience but as integral to the development process.

• Collaboration: Working closely with Sales, Product, and other Engineering teams to ensure successful outcomes.

• Impact: Valuing tangible results and immediate contributions to the product and company's growth.

Collaboration Style:

• Cross-Functional Integration: Seamless collaboration with Sales and Product teams is essential for understanding requirements and aligning prototypes with the roadmap.

• Pairing and Code Reviews: Expectation of close collaboration within the engineering team, including code reviews, to maintain quality and share knowledge.

• Open Communication: Encouraging sharing of demos and proposals for follow-on work, fostering a transparent and communicative environment.

• Respectful Disagreement: Comfort with being "respectfully wrong out loud" suggests a culture that values open discussion and learning, even when challenging ideas.

📝 Enhancement Note: The company culture appears to value pragmatic innovation, where cutting-edge tools like AI are used to deliver tangible business value rapidly, but without sacrificing engineering quality or customer understanding.

⚡ Challenges & Growth Opportunities

Challenges:

• Balancing Speed and Quality: The primary challenge will be maintaining high production code standards while iterating rapidly with AI-assisted prototyping. Ensuring prototypes are "clean enough to ship" requires constant vigilance.

• AI Limitations: Identifying and correcting the subtle errors that AI can introduce will be a key technical challenge, requiring strong debugging and critical thinking skills.

• Translating Ambiguity: Effectively interpreting and translating vague customer requests into concrete, executable prototypes requires excellent communication and problem-solving abilities.

• Integration Complexity: Seamlessly integrating prototypes into a mature production codebase without disrupting existing functionality or introducing technical debt.

• Market Responsiveness: Staying ahead of customer needs and rapidly developing solutions in a competitive technology landscape.

Learning & Development Opportunities:

• AI Advancement: Opportunities to explore and master the latest AI coding tools and techniques, becoming an expert in AI-assisted software development.

• Domain Expertise: Deepen knowledge in mining technology, robotics, or industrial applications where the company operates.

• Full-Stack Exposure: Gain experience across the software stack, from backend services (Python, C++) to potentially frontend applications and cloud infrastructure (AWS).

• Hybrid Role Mastery: Develop a unique skill set combining deep engineering expertise with GTM acumen, opening doors to specialized roles.

• Startup Growth Experience: Contribute to and learn from the rapid growth of a promising technology startup.

📝 Enhancement Note: This role presents a significant opportunity for engineers who enjoy fast-paced problem-solving and want to push the boundaries of AI in software development, while also having a direct impact on customer success and product evolution.

💡 Interview Preparation

Strategy Questions:

• AI Integration Strategy: "Describe a complex problem you solved using an AI coding assistant. What was the problem, how did you use AI, what were the limitations, and how did you ensure the final code was production-ready?"

• Prototype-to-Production Process: "Walk me through your process for taking a rapid prototype and integrating it into a production codebase. What are the key considerations for quality, architecture, and team buy-in?"

• Customer Needs Translation: "Imagine a customer describes a need vaguely. How would you approach understanding their true requirement, demonstrating potential solutions, and prototyping effectively?"

• Handling AI Errors: "Share an example where AI-generated code had subtle errors. How did you identify them, and what was your strategy for debugging and correcting them?"

Company & Culture Questions:

• Motivation for Hybrid Role: "Why are you interested in a role that blends rapid prototyping, AI, and direct customer/sales interaction, rather than a traditional software engineering role?"

• Team Collaboration: "Describe a time you collaborated with sales or product teams. How did you ensure effective communication and alignment on technical deliverables?"

• Measuring Success: "How would you measure the success of a rapid prototype? What metrics would you track, and how would you determine if it's ready for production?"

Portfolio Presentation Strategy:

• Focus on AI & Speed: Dedicate sections of your presentation to showcasing projects where AI significantly accelerated development or where you delivered functional prototypes rapidly.

• Demonstrate Code Quality: For production code examples, be prepared to discuss testing strategies, design patterns, and how you ensure maintainability.

• Customer Impact Stories: Frame your project examples around the customer problem solved or the business value delivered, especially for prototypes.

• Interactive Walkthrough: Be ready to dive into specific code snippets or architectural diagrams to explain your technical decisions and problem-solving approach.

• Address the "Refactoring" Aspect: Clearly articulate how you approach taking less-than-perfect (prototype) code and transforming it into robust, production-ready solutions.

📝 Enhancement Note: The interview preparation should heavily emphasize practical demonstrations of AI tool usage, speed of delivery, and the ability to bridge technical development with customer needs. Be ready to showcase your code and articulate your thought process clearly.

📌 Application Steps

To apply for this operations position:

• Submit your application through the provided application link.

• Portfolio Customization: Tailor your resume and portfolio to highlight specific projects involving rapid prototyping, AI tool integration (mentioning tools like Claude Code CLI or Codex), and contributions to production codebases. Quantify your achievements wherever possible.

• Resume Optimization: Ensure your resume clearly states your proficiency in Python and/or C++, your years of professional software engineering experience, and any relevant domain knowledge (mining, robotics). Use keywords from the job description naturally.

• Interview Preparation: Practice articulating your experience with AI coding assistants, your approach to refactoring, and your ability to communicate technical concepts to non-technical stakeholders. Prepare concrete examples for potential interview questions.

• Company Research: Thoroughly research Mine Vision Systems, their products (FaceCapture), their industry (mining technology), and their mission. Understand how your skills align with their goal of transforming underground mining operations through data and AI.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and operations industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.

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