Senior Machine Learning Engineer

Our Company

The U.S. construction permitting process is a black box. Over 500,000 forms and 20,000 processes, yet 95% of cities use the same building code. Every permitting delay costs businesses revenue, stalls expansion, and disrupts construction schedules.

Founded in 2022 by builders, entrepreneurs, and industry experts, Green Lite eliminates permitting uncertainty for developers nationwide.

We combine AI-powered technology with an in-house team of registered architects, engineers, and city planners to deliver the fastest, most predictable path to permit approval. Our expert‑led compliance process ensures plans are code‑ready before submission, reducing revisions, delays, and costs.

Green Lite has raised $86M in venture funding from leading investors including Insight Partners, Energize Capital, Craft Ventures, Live Oak Ventures, Trust Ventures, and Chicago Ventures. National brands, including Walgreens, TD Bank, and Driven Brands, trust us to accelerate approvals, reduce risk, and unlock growth.

We're on a mission to automate one of the most costly and expertise‑dependent bottlenecks in the built environment — construction plan review. Today, plan review is slow, expensive, and highly manual, requiring licensed experts to navigate thousands of unique jurisdictional construction codes and complex architectural documents. We believe AI can help.

As a key hire in our AI engineering organization, you’ll operate with a founder mindset, help in defining our long‑term data strategy, and help to grow multiple squads behind it. Green Lite is building agentic workflows that compress permitting cycle time across thousands of jurisdictions—spanning permit intake, checklist generation, document QA, code citation, and review ops inside Lite Table. Your work turns messy PDFs/CAD plans and scattered code texts into decisive, auditable agent actions that help customers get permits faster.

Our agent stack leans on:

• Bedrock Agent Core to deploy and operate agents securely at scale with session isolation, long‑running workloads, built‑in tools, memory, identity, gateway, and observability.
• Lang Graph for graph‑based orchestration and error‑tolerant control flow.
• Strands Agents for structured reasoning and tool use.

• Design & ship production agents: Own one or more high‑impact agent workflows (e.g., Permit Intake Triage, Smart Document QA, Compliance Comment Copilot, Code Lookup inside Lite Table). Compose multi‑step graphs (Lang Graph) with Strands‑based reasoning/tooling and develop internal/external tools for targeting heterogeneous datasets.
• Operationalize on Bedrock Agent Core: Use Runtime for secure, scalable hosting and streaming;
  Gateway to expose APIs as agent‑ready tools;
  Memory for persistent context;
  Identity for least‑privilege access;
  Observability for traces/metrics; and built‑in Browser/Code‑Interpreter where appropriate.
• Evaluation & safety harness: Stand up task‑level and end‑to‑end evals (success rate, cost, latency, human‑handoff rate) and regression suites; borrow ideas from Agent Bench/Web Arena/SWE‑bench where useful but bias toward domain‑grounded tests for permitting.
• Retrieval & knowledge: Partner with Data to wire agents to building‑code knowledge (RAG), evaluate vector store options (incl.
  S3 Vectors preview, or Pinecone
  ) and set up durable knowledge interfaces the agents can trust.
• UX handoffs: Collaborate with Product/Design to craft agent‑first reviewer UX in Lite Table (great inline citations, diffs, and remediation suggestions).
• Quality & reliability: Build fault‑tolerant flows (retries/rollbacks/compensation), observability dashboards, and on‑call runbooks for agent incidents.
• Technical leadership: Create internal libraries/templates for agent patterns; mentor engineers; review designs with domain experts (architects/code officials).

• Agent frameworks: Depth with Lang Graph (graph orchestration, state, recovery) and Strands Agents (structured tool reasoning); experience integrating MCP clients/servers.
• Production chops: 7–12+ years shipping high‑reliability backend or ML systems (Python/Type Script), cloud infra (AWS), containers…