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Scaffold

Senior Machine Learning Engineer

Posted 3 Days Ago
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In-Office
Austin, TX, USA
Senior level
In-Office
Austin, TX, USA
Senior level
Design, build, and deploy production machine learning systems across workflow automation, data enrichment, predictive insights, and agent-based workflows. Own the full ML lifecycle, including data ingestion, model training, evaluation, deployment, and feedback loops. Integrate AI into backend services, APIs, data pipelines, and customer-facing products while shaping architecture and long-term AI strategy. Collaborate with product, engineering, and customers to deliver scalable, reliable AI solutions.
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We are hiring a Machine Learning Engineer to design, build, and deploy AI systems that sit at the core of Scaffold’s platform.

This is not a “model-only” role. You will operate as a product-minded engineer, working across data pipelines, model development, and production systems to deliver real-world impact.

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You will help define how AI is applied across:

  • Workflow automation (POs, schedules, change orders)

  • Data normalization and enrichment

  • Predictive insights (delays, risk, capacity)

  • Agent-based systems interacting with builder/trade ecosystems

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About Scaffold

Scaffold is building the data orchestration layer for residential construction. We integrate fragmented systems across builders, subcontractors, and suppliers to automate workflows, eliminate manual work, and unlock real-time decision-making.

We are now embedding AI deeply into this system — transforming raw, messy construction data into intelligent automation, predictive insights, and agent-driven workflows.

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‍What You’ll Do:

  • Build AI-Powered Product Features

    • Design and deploy machine learning models that operate on real-world construction data

    • Develop LLM-powered workflows for document parsing, communication, and automation

    • Build intelligent agents that interact with external systems (builder portals, ERPs

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  • Own the End-to-End ML Lifecycle

    • Ingest, clean, and structure messy third-party data

    • Train, evaluate, and iterate on models in production

    • Design feedback loops to continuously improve performance

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  • Work Across the Stack

    • Integrate ML systems into backend services and APIs

    • Collaborate on data pipelines and infrastructure

    • Partner with frontend engineers to expose AI capabilities in product

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  • Drive Architecture & Strategy

    • Evaluate model approaches (LLMs, fine-tuning, retrieval, traditional ML)

    • Define scalable patterns for deploying AI across customers

    • Contribute to long-term AI roadmap and product direction

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  • Collaborate Cross-Functionally

    • Work closely with product, engineering, and customers

    • Translate ambiguous problems into production-ready solutions

    • Provide technical leadership on AI initiatives

What We’re Looking For:

  • Core Requirements

    • 3–6+ years of experience in ML engineering, applied AI, or related roles

    • Strong programming skills (Python required; familiarity with Node/TypeScript a plus)

    • Experience building and deploying ML models in production environments

    • Familiarity with LLMs, prompt engineering, and modern AI tooling

    • Strong understanding of data pipelines, APIs, and distributed systems

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  • Preferred Experience

    • Experience with:

      • LLM frameworks (LangChain, LlamaIndex, etc.)

      • Vector databases / retrieval systems

      • Data processing at scale (Spark, Airflow, etc.)

    • Exposure to real-world noisy data environments (not just clean datasets)

    • Experience building internal tools or automation systems

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  • Mindset

    • Product-oriented: you care about outcomes, not just models

    • Systems thinker: you design for scale and reliability

    • Builder: you take ownership and move fast in ambiguous environments

    • Curious: you stay on the frontier of AI capabilities

Why Join Scaffold?

  • Impact: Play a critical role in defining and building the future of Scaffold and the residential construction industry.

  • Growth Opportunity: Be an early leader in a venture-backed company.

  • Culture: Work alongside high-caliber teammates in a no-ego, high-energy environment.

  • ‍Compensation: Competitive salary and meaningful equity in the company.

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