About Loop

Loop is on a mission to unlock profits trapped in the 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.

About The Role

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. 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 backend features. 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 Can Span 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 engineer – 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-tuning multimodal LLMs to reduce hallucinations.
Build out an agent that audits freight invoices.

Qualifications

1+ years of hands-on experience in deep learning frameworks (e.g., PyTorch, Tensorflow, etc.)
Solid understanding of fundamental ML algorithms, especially 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.

Nice to Have

Prior startup experience.
End-to-end experience building out machine learning models and features.

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