We're an AI-native company leveraging AI to automate complex back-office workflows and tasks in the freight industry. Our mission is to unlock profits and lower costs by addressing the bad data and inefficient workflows that plague the supply chain. We're expanding our AI team and are looking for a talented AI Engineer to build both AI models and features that directly impact our business. The role is broad, offering the opportunity to work on everything from training and deploying in-house multimodal Large Language Models (LLMs) to scaling our inference infrastructure and building AI agent workflows.
What You'll Work On
Your primary focus will be on
document extraction and understanding , using multimodal LLMs to extract, normalize, and link data with a high degree of accuracy. You'll also work on scaling our machine learning platform to handle significant increases in training and inference volume. Future projects will include expanding our AI capabilities into workflow automation and audit using AI agents.
Responsibilities
ML modeling: Training, evaluating, and deploying models
ML infrastructure: Scaling data infrastructure and improving the reliability of our ML systems
AI engineering: Orchestrating API LLM models to solve business problems
Backend engineering: Building atomic tasks and general backend work in the automation domain
Qualifications
1-3 years of experience with deep learning frameworks (e.g., PyTorch, TensorFlow)
Open to recent graduates with a Computer Science degree from a top university
This is a hybrid role based in our San Francisco office, with the expectation of being in the office 3-5 days per week
Benefits
Compensation & Benefits
Salary: $130k - $200k
Visa sponsorship is not mentioned as available
Potential Projects
Scaling the throughput of our inference engine through techniques like continuous batching
Developing methods to fine-tune multimodal LLMs to reduce hallucinations
Building AI agents that audit freight invoices
Equality and Accessibility
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