AI Engineer

Loop; United States; Remote; USD 80,000 - 120,000; Full time

Job summary

A leading technology company in logistics is looking for an expert to join its AI team. In this role, you will focus on building and deploying AI models that solve complex problems and ensure the reliability and scalability of machine learning systems. Responsibilities span ML modeling, infrastructure, and backend engineering, with projects involving document extraction and workflow automation.

Qualifications

• Experience with machine learning frameworks and libraries.
• Proficiency in training and deploying AI models.
• Strong understanding of backend development principles.

Responsibilities

• Train, evaluate, and deploy machine learning models.
• Scale data infrastructure to improve ML system reliability.
• Utilize API LLM models to solve business challenges.

Skills

ML modeling

ML infrastructure

AI engineering

Backend engineering

Job description

Overview

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 AI agent workflows. In doing so, you’ll have the opportunity to define how the AI and broader Loop team will grow.

What you will work on

Our primary focus has been on document extraction and understanding, where we utilize multimodal LLMs 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.
• ML infrastructure – scaling data infrastructure, training, or inference, improving reliability of ML systems at Loop.
• AI engineering – utilizing and orchestrating API LLM models to solve business problems at Loop.
• Backend engineering – building out atomic tasks, general backend work in the servicing or automation domain.

Some projects you might work on:

• Scaling up throughput of Loop’s inference engine through continuous batching.
• Developing ways to fine-tune multimodal LLMs to reduce hallucinations.
• Build out agents that audit freight invoices.