Role Description We are hiring a highly execution-focused Senior Technical Developer (Contract) to own the technical and systems layer of an enterprise project controls platform. Our software replaces the messy spreadsheet-and-email toolchain that dominates megaproject oversight with a single production-live system serving major capital construction programs. Because our data directly informs client decisions worth millions of dollars, absolute data accuracy and system reliability are core table stakes. This role is ideal for an implementation partner with expert-level TypeScript and React fundamentals who wants to step directly into a production codebase and build out real-world ML/AI infrastructure (including fine-tuned LLMs and voice interactive pipelines)—not just wrap basic APIs. You will work directly with the founder in an environment completely free of ticket theater and management layers. - Act as the primary implementation partner working directly in a production codebase. - Own the building, engineering, and feature deployment of full-stack system features. - Design, implement, and submit clean Pull Requests (PRs) for review. - Build and maintain robust machine learning pipelines, covering training data preparation, fine-tuning scripts, and system evaluations. - Write complex raw PostgreSQL database schemas, queries (CTEs, window functions), and data migrations. - Develop, scale, and optimize high-throughput model inference steps and custom voice pipelines (STT/TTS). - Decompose feature briefs, propose structural technical architectures, and autonomously resolve backend production bugs. Qualifications - 3+ years of production experience with strict TypeScript/JavaScript (generics, type inference, discriminated unions). - Production React experience with clear ownership over shipping and maintaining real, live web applications. - Next.js App Router literacy, demonstrating deep familiarity with server vs. client components, middleware, and route handlers. - SQL Fluency with raw PostgreSQL; you must have zero dependency on an ORM. - Python proficiency explicitly tied to data preparation, training, and evaluation for machine learning pipelines. - Strict Git workflow discipline, maintaining clean commits and structural code review habits. - Familiarity or deep learning trajectory inside ML infrastructure frameworks (personal projects, fine-tuning experiments, or coursework). Skills - Strong independent research skills to learn unfamiliar technical stacks and specialized domains without hand-holding. - Excellent written English communication skills for documentation, thorough code reviews, and technical notes. - High technical agency to fill the gaps given an ambiguous brief, proposing sensible fallback paths independently. - Meticulous attention to detail: you test your own edge cases and ensure work is right the first time. Nice-To-Have - Hands-on PyTorch and Hugging Face model fine-tuning (PEFT/LoRA, Transformers library). - Voice/speech ML engineering background (STT, TTS, or end-to-end voice pipeline tuning). - GPU infrastructure management capabilities (self-hosted GPU servers, CUDA, resource monitoring). - Front-end data visualization expertise (Tailwind CSS, Radix unstyled primitives, complex dashboard trend charting). - Automated document report generation systems (PDF, Excel, Word exports). - DevOps exposure: Docker containerization, multi-stage builds, and automated CI/CD pipelines. - Prior domain exposure to capital construction, industrial software, or engineering project controls. Success Metrics - Day 30: Local environment completely running, 5–8 production PRs successfully merged, full data tracing verified from the database to the UI, and basic domain familiarity achieved. - Day 60: Autonomously implementing core features, writing raw SQL migrations confidently, handling active production bugs, and launching initial LLM fine-tuning experiments. - Day 90: Decomposing broad feature briefs into standalone technical tasks, proposing architecture modifications, showing full-stack comfort, and actively contributing to voice AI and ML infrastructure pipelines. Why This Role Stands Out - Fully remote flexibility across global talent pools. - Direct impact on enterprise assets: Your code directly influences active capital construction programs worth millions of dollars. - Production ML experience: Opportunity to fine-tune LLMs, build real voice pipelines, and manage GPU inference server clusters on actual running systems, not simple demo tools. - Specialized domain depth: Gain high-value expertise in a specialized, multi-billion dollar industrial sector that most developers never get to touch. - Modern, zero-bloat stack: Build with fresh, current tools applied to complex engineering problems without corporate ticket theater or management bottlenecks. How to Apply - Apply with a brief introduction (2–3 paragraphs) explaining your technical background and why this specific intersection of full-stack engineering and ML infrastructure stands out to you. - Links to your active work (GitHub, technical portfolios, or production application code examples). - Your availability profile (Earliest start date, target rate expectations, weekly hours capacity, and timezone location).

• Support a knowledge engineering and content transformation function focused on improving the documentation lifecycle through AI-enabled tools, automation, and modern docs-as-code practices. • Help plan, develop, test, and improve AI-powered documentation solutions, including agent skills, automation workflows, internal knowledge resources, and technical content programs. • Comfortable writing and maintaining developer-facing content while also working in technical environments that include Markdown, Git-based collaboration, CI/CD publishing workflows, scripting, and AI-assisted coding tools. • Plan, scope, and manage technical content programs from ideation through authoring, review, user acceptance testing, publication, and ongoing maintenance. • Author, edit, and review technical content such as internal wiki articles, how-to guides, UAT plans, process documentation, and contributor guidance. • Support docs-as-code workflows using Markdown, code editors, Git-based collaboration, pull requests, issues, and CI/CD publishing processes. • Facilitate user acceptance testing, gather participant feedback, document findings, and recommend next steps. • Monitor adoption, usage, feedback, and success metrics for documentation tools, agent skills, and AI-enabled content workflows. • Track documentation quality indicators such as engagement, satisfaction, time-to-resolution, content freshness, accessibility, and issue resolution. • Triage and resolve content bugs from feedback channels, analytics, and issue backlogs. • Partner cross-functionally with content, engineering, product, design, and transformation stakeholders to coordinate workstreams and ensure documentation accuracy. • Apply content quality standards, including voice and tone, accessibility, information architecture, metadata, taxonomy, localization readiness, and content reuse practices. • Use technical authoring and publishing tools such as Visual Studio Code or similar editors, Markdown linters, static-site publishing systems, and documentation repositories. • Apply software engineering practices such as branching strategies, code review, automated validation, and testing to content development workflows. • Use AI-assisted and agentic coding tools to accelerate content creation, automate repetitive documentation tasks, and prototype workflow improvements. • Identify opportunities to integrate AI-assisted authoring, content generation, quality assurance, and automation into the documentation lifecycle. • Contribute to or build lightweight scripts, tools, extensions, or automation using languages such as Python, JavaScript, TypeScript, or similar technologies.

• Document data models, structured vocabularies, controlled terminology, and the relationships between data objects. • Produce user-focused guides and reference materials explaining how to navigate and interact with the tools, data structures, and knowledge frameworks developed by the project team. • Create clear descriptions of end-to-end workflows, including actors involved, information transformations, system interactions, and decision logic. • Develop training content, presentations, and demonstrations that assist stakeholders in understanding and adopting new digital workflows and data structures. • Synthesize and organize inputs from SME interviews, workshops, working groups, and technical reviews into structured documentation. • Document design considerations, integration points, interoperability requirements, and risk-related guidance provided by engineering and architecture teams. • Produce supporting materials for project reviews, technical exchange sessions, and recurring status updates. • Other duties as assigned

• Support the FAA by documenting the impact of Counter Unmanned Aerial System (CUAS) activities on Air Traffic Operations • Providing technical writing support in documenting and supporting development of policies, procedures, or protocols to minimize risks to the safe and efficient flow of air traffic posed by CUAS operations • Tracking and reporting the operational impact of CUAS activities • Suggesting policies in need of documenting • Assisting the team with review of documentation • Reviewing and editing responses to UAS Security inquiries from OIG, Congress, and ATO Leadership • Provide support to ensure the quality of deliverables and products • Collect, analyze, coordinate, and disseminate UAS security related data and evaluate how UAS impact the security work of AJR-24 • Develop or modify plans, procedures, SOPs, work tools, orders, and other documents related to addressing the operations of UAS and the concomitant implications of UAS Operations in the NAS • Develop products that document plans, procedures, methods, techniques, agreements, and other artifacts resulting from UAS security work • Support the FAA Records and Information Management (RIM) Program as assigned