Description
The Opportunity
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.
- Work Model: Remote | Contract
- Time Commitment: 25–40 hours / week
- Timezone Requirement: EST/CST
- Compensation: Open
What You’ll Own
- 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.
Requirements
Experience
- 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
Ramp-Up Milestones
- 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.
Benefits
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.
If you enjoy building advanced full-stack systems, optimizing data pipelines, and take pride in getting architecture right without supervision, we want to hear from you.
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).
