Job Description

About the Company:

Our client is reinventing how companies manage freight audit and payment. Their mission is to unlock profit trapped in the supply chain by replacing inefficient workflows and legacy systems with domain-driven AI that automates complex back-office logistics tasks.

Their intelligent platform helps large enterprises understand, control, and optimize transportation spend. Backed by J.P. Morgan, Founders Fund, Index Ventures, 8VC, and operators from Flexport, Uber, Meta, and Google, our client is growing fast with industry leaders like J.P. Morgan Chase, Great Dane, and Loadsmart already on board. With over $65M raised, Loop is positioned to disrupt one of the most complex markets on Earth.

Roles and Responsibilities:

As an AI Engineer, the selected candidate will be responsible for building and scaling Loop’s AI-driven infrastructure that powers everything from multimodal LLMs to document intelligence and audit agents. The team is tackling deeply technical challenges—from fine-tuning large models to building practical agent workflows—all with a strong bias toward shipping fast and delivering measurable business impact.

Key responsibilities include:

• Training, fine-tuning, and deploying multimodal LLMs for document parsing and understanding.
• Integrating and orchestrating LLM APIs (e.g., OpenAI, Claude, Gemini) into production services.
• Collaborating cross-functionally with product, engineering, and infrastructure teams to ship features that deliver AI-powered insights.
• Improving reliability, scalability, and performance of inference engines for high-volume throughput.
• Building agent-based systems for invoice auditing, data normalization, and workflow automation.
• Scaling ML infrastructure (training and inference), improving fault tolerance, and enabling observability for deployed AI systems.
• Writing production-grade backend code in Python and other relevant technologies.

Example projects include:

• Scaling their inference engine with batched real-time processing.
• Reducing hallucinations in custom fine-tuned LLMs.
• Developing agents that automate freight invoice validation and reconciliation.
  

Job Requirements:

• 1–2+ years of hands-on experience building applied AI/ML systems.
• Strong understanding of deep learning frameworks (e.g., PyTorch, TensorFlow).
• Experience with LLM APIs, fine-tuning, prompt engineering, or building agentic workflows.
• Solid programming skills (Python preferred) and engineering fundamentals.
• Experience deploying ML to production (not just research or experimentation).
• Background working with cloud environments (AWS, GCP, or Azure).
• Bachelor’s degree in CS, AI, or related field from a top-tier institution.
  

Bonus if the candidate has:

• Experience with multimodal data (text + visual inputs).
• Exposure to workflow automation in enterprise or logistics settings.
• Familiarity with vector databases, semantic search, or RLHF pipelines.
  

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