Senior Full-Stack ML Engineer - Remote

Description The Opportunity

We are hiring a highly execution-focusedSenior 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 mega project 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 Type Script 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

• 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.

• 3+ years of production experiencewith strict Type Script/JavaScript (generics, type inference, discriminated unions).
• Production React experiencewith 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 proficiencyexplicitly 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 trajectoryinside ML infrastructure frameworks (personal projects, fine-tuning experiments, or coursework).

• 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.

• 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).
• Dev Ops exposure:
  Docker containerization, multi-stage builds, and automated CI/CD pipelines.
• Prior domain exposure to capital construction, industrial software, or engineering project controls.

• Day 30:Local environment completely running, 5–8 production PRs successfully merged, full data tracing…