AI Engineer

Loop; Deutschland; Remote; EUR 60.000 - 80.000; Vollzeit

Zusammenfassung

A dynamic technology company in Germany is seeking an AI Engineer to develop machine learning models and infrastructure. You will work on diverse projects like scaling ML systems and creating AI agents for workflow automation. Ideal candidates have experience in ML modeling and backend engineering, with a strong focus on data reliability and accuracy. This role offers a unique opportunity to directly influence the growth of AI solutions in a fast-paced environment.

Qualifikationen

• Experience in training and deploying machine learning models.
• Strong background in backend engineering and data infrastructure.
• Knowledge of AI engineering practices and API orchestration.

Aufgaben

• Build and deploy AI models impacting business decisions.
• Develop ML infrastructure for reliability and scalability.
• Create AI agents for automating auditing processes.

Kenntnisse

Machine Learning

Backend Engineering

API Integration

Data Infrastructure Scaling

Jobbeschreibung

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-tuning multimodal LLMs to reduce hallucinations.

• Build out agents that audits freight invoices.