Senior Technical Developer at MiraModo Inc. -- 2

MiraModo Inc. Prepared: August 2026 Senior Technical Developer MiraModo Inc. • Product: Insara • Remote • Contract Company Overview MiraModo Inc. (Houston, TX, founded 2024) builds Insara, a project controls platform for capital construction. Insara replaces the spreadsheet-and-email toolchain that dominates megaproject oversight with a single system for earned value management, schedule analysis, field progress tracking, cost forecasting, and change management. The platform is production-live with active enterprise deployments on major capital programs. It directly informs decisions worth millions of dollars, so data accuracy and reliability are table stakes. We're also building our own ML infrastructure: a custom fine-tuned LLM for construction project controls and a custom voice pipeline for field-facing AI interaction. The Role Title Senior Technical Developer (Contract) Commitment 25–40 hours / week Location Remote (global). US Central/Eastern timezone overlap required. Scope Full-stack development + ML/AI infrastructure Reports to Founder (direct collaboration, no management layers) You'll be the founder's primary implementation partner, working directly in a production codebase that serves enterprise clients. The founder handles product direction, AI architecture, and client relationships. You own the building. Growth Path • Months 1–2 (Guided): Founder specs tasks. You design, implement, and submit PRs for review. • Months 3–4 (Semi-Autonomous): You take broader briefs, propose architecture, handle production issues with less oversight. • Months 5+ (Trusted Partner): You independently scope and deliver features and provide input on technical direction. Technical Requirements Detailed architecture and system documentation provided to selected candidates under NDA. Core Stack TypeScript (strict) - 3+ years production; generics, discriminated unions, type inference - Required Python - Production proficiency for ML pipelines, training, evaluation - Required React (latest) - Hooks, server components, component architecture; shipped production apps - Required [login to view URL] App Router - Server vs. client components, route handlers, middleware - Required PostgreSQL (raw SQL) - Complex queries, CTEs, window functions, schema design, migrations; no ORM - Required Type-safe APIs - End-to-end type safety between client and server (e.g. tRPC) - Required Git workflow - Clean commits, PR-based development, code review discipline - Required UI & Visualization Tailwind CSS - Utility-first CSS, responsive, data-dense layouts - Preferred Component libraries - Headless / unstyled component primitives (e.g. Radix) - Preferred Data visualization - Charting libraries, dashboards, S-curve and trend charts - Preferred Report generation - PDF, Excel, Word, PowerPoint export - Preferred ML / AI Infrastructure PyTorch & Hugging Face - Model fine-tuning, PEFT/LoRA, Transformers library - Preferred Voice / speech ML - STT, TTS, end-to-end voice pipeline development - Preferred GPU infrastructure - Self-hosted GPU servers, CUDA, resource monitoring - Preferred Inference serving - High-throughput model serving, quantization, optimization - Preferred Evaluation - Standard and custom domain-specific evaluation harnesses - Preferred Infrastructure & DevOps Docker - Containerization, multi-stage builds, compose - Preferred CI/CD pipelines - Automated build, test, security scan, deployment workflows - Preferred Self-hosted deployment - VPS-based hosting, container orchestration, monitoring - Bonus Testing - Unit and integration testing frameworks - Preferred Note: Strong TypeScript + strong ML is a rare combination. A candidate with solid TS/React/SQL fundamentals and a demonstrated ML learning trajectory (personal projects, coursework, fine-tuning experiments) is an excellent fit. Aptitude and drive matter more than arriving with deep ML expertise. Domain: Construction Project Controls The professional domain is capital construction project controls: measuring, forecasting, and managing cost and schedule performance on large construction programs. You don't need prior domain experience, but you must be willing to learn it. Concepts Earned Value Management - Industry-standard methodology for measuring project performance by comparing planned work, completed work, and actual cost. Schedule Analysis - Critical path analysis, schedule forecasting, delay identification, forensic scheduling. Field Progress Measurement - Objective methods for quantifying physical completion using predefined measurement criteria. Cost Management - Budgeting, cost tracking, forecasting, variance analysis, S-curve trend visualization. Change Management - Scope change tracking, information requests, deficiency management and their impact on cost/schedule. Work Packaging - Organizing construction activities into executable packages across engineering, procurement, and construction. Each concept maps directly to platform features. Domain knowledge is trained during onboarding with documentation, guided tasks, and founder mentorship. Working Model Hours - 25–40 hrs/week (close to full-time expected) Timezone - Global candidates welcome; must overlap US Central/Eastern daytime (~9 AM to 5 PM CT) Communication - Async daily (Slack/Discord) + video calls as needed for architecture discussions Code workflow - Feature branch ? PR ? founder review ? merge ? automated deployment Tooling - AI-assisted IDE, GitHub, Docker, PostgreSQL client Ramp-Up Milestones 30 Days - Environment running. - 5–8 PRs merged. - Can trace data from database to UI. - Basic domain familiarity. 60 Days - Independently implementing features. - Writing SQL migrations confidently. - Handling production bugs. - First ML fine-tuning experiments. 90 Days - Decomposing feature briefs into tasks. - Proposing architecture. - Full-stack comfort. - Contributing to voice pipeline and ML infrastructure. Candidate Profile Must-Have Attributes • 3+ years production TypeScript/[removed] strict mode, generics, type inference • Python proficiency for ML pipeline work (training, evaluation, data prep) • Production React experience. Must have shipped and maintained real applications. • [login to view URL] App Router understanding (server components, route handlers, middleware) • SQL fluency with raw PostgreSQL; no ORM dependency • Experience with (or strong interest in) LLM fine-tuning and ML infrastructure • Clean Git workflow and PR-based development discipline • Clear written English for code review, docs, and occasional client communication • Can learn independently across a specialized domain and fast-moving ML engineering Differentiating Attributes • Hands-on PyTorch / Hugging Face model fine-tuning • GPU infrastructure management (provisioning, CUDA, monitoring) • Voice/speech ML: STT, TTS, or voice pipeline development • Inference optimization (quantization, high-throughput serving) • Data visualization and chart-heavy dashboard development • Docker, CI/CD, and container-based deployment • Construction, engineering, or industrial domain exposure What We Offer • Direct impact: Your code serves live clients on active capital programs • Production ML: Fine-tune LLMs, build voice pipelines, manage inference. Real systems, not demos. • Domain depth: Learn a specialized, high-value field most developers never touch • Growth path: From guided implementation to trusted technical partner • Modern stack: Current tools applied to real problems, not CRUD apps • Founder-direct: No management layers, no ticket theater How to Apply 1. Brief introduction (2–3 paragraphs): Who you are, why this role interests you, relevant experience. 2. Links to your work: GitHub, portfolio, or production application examples. 3. Availability: Weekly hours, timezone, earliest start date, rate expectations. Screening Questions SQL Question (required, 4–8 sentences): You have a PostgreSQL table work_items (id, category_id, discipline, budgeted_hours) and a table progress_entries (id, work_item_id, milestone_index, completed_at, credit_percent). Write a SQL query that calculates total earned hours per discipline for a given category_id. Explain your approach. ML Question (optional, 4–8 sentences): You are fine-tuning an LLM for a specialized technical domain where the base model frequently produces incorrect terminology. Describe your approach: training data preparation, fine-tuning strategy, and how you would evaluate whether the model has measurably improved. Selected candidates will receive detailed technical documentation under NDA during onboarding. Insara is where full-stack engineering meets ML infrastructure meets a specialized domain that actually matters. We're building our own LLM and voice AI, not just wrapping APIs. If that sounds interesting, we want to hear from you. Construction and ML are both learnable. What isn't optional is caring about getting it right.

Project ID: 40733614