AI Engineer

Loop is on a mission to unlock profits trapped in the supply chain (https://loop.com/article/unlock-profit-trapped-in-your-supply-chain) and lower costs for consumers. Bad data and inefficient workflows create friction that limits working capital and raises costs for every supply chain stakeholder.

Loop’s modern audit and pay platform uses our domain-driven AI to harness the complexity of supply chain data and documentation. We improve transportation spend visibility so companies can control their costs and power profit. That is why industry leaders like J.P. Morgan Chase, Great Dane, Emerge, and Loadsmart work with Loop.

Our investors include J.P. Morgan, Index Ventures, Founders Fund, 8VC, Susa Ventures, Flexport, and 50 industry-leading angel investors. Our team brings subject matter expertise from companies like Uber, Google, Flexport, Meta, Samsara, Intuit, Rakuten, and long-standing industry leaders like C.H. Robinson.

Loop is growing its AI team and you’ll have the opportunity to build both AI models and features that directly impact Loop’s business. Loop is positioned to disrupt the incumbent freight audit and pay market, and is one of the companies leading the AI services wave, utilizing AI to automate complex back-office workflows and tasks. You will face and solve many complex technical challenges while you receive guidance and feedback from the team. The range of work here is broad, you can work on everything from training and deploying in-house multimodal LLMs, scaling our inference infrastructure, or building out and shipping agentic workflows. In doing so, you’ll have the opportunity to define how the AI and broader Loop team will grow. 

Our primary focus has been on document extraction and understanding, where we utilize multimodal LLMs, including our own in-house foundation model, to extract, normalize, and link data together into our domain model. As Loop’s customers rely on Loop to ingest and normalize highly accurate data, we hold ourselves to a high standard to build models with a very high level of accuracy. In tandem, Loop’s machine learning platform requires a high degree of reliability and scalability, and we expect our training and inference volume to scale several orders of magnitude in the coming year. Going forward, Loop will expand its AI capabilities, expanding into other areas such as workflow automation and audit, where we will utilize agents to tackle these problems. 

This role spans multiple domains:

• →ML modeling – training, evaluating, and deploying models.
• →AI engineering – utilizing and orchestrating API LLM models to build agents to solve business problems at Loop.
• →Backend engineering – building out frameworks to enable AI agents at scale.

• 1+ years of hands-on experience in deep learning frameworks (e.g., PyTorch, Tensorflow, etc.) or shipping agents to production.
• Solid understanding of fundamental ML algorithms, especially multimodal LLMs.
• Experience fine-tuning LLMs and deploying them to production or building out agentic systems.
• Ability to ship high-quality code to production.
• Experience in cloud environments (AWS, Google, Azure).
• Strong communication skills and willingness to collaborate in a cross-functional team environment with domain experts.
• Keep up to date with the latest AI research.
• Ability to work in a fast-paced environment.