Role overview
We are looking for an
AI Engineer
who thrives in one of the most demanding environments in technology: legacy supply chains, enterprise customers, startup speed, and chaotic real-world operations. At Vorto, AI Engineers are builder-owners who transform ambiguous, messy operational problems into intelligent, automated systems that materially change how physical goods move across America.
You will work across the full AI stack — from model training and agent design to retrieval pipelines and high-performance inference — to architect solutions that automate decisions previously handled by entire teams. This role blends deep ML expertise with practical engineering: roughly 70% hands-on software development and 30% advanced model development and integration.
Success requires 10× velocity, first-principles thinking, strong fundamentals, and the grit to solve real-world problems where data is imperfect, conditions change hourly, and the cost of failure is high. AI Engineers at Vorto own outcomes end-to-end, recover fast, and build systems that scale beyond themselves.
If you want to build AI that actually runs the real world — not demos, not prototypes, but production systems moving billions in freight — this is the role.
What you'll work on
- Design and develop production level backend software used by our automated agents.
- Design and deploy AI agents capable of task execution and decision-making, leveraging retrieval-augmented generation (RAG) to improve accuracy and relevance.
- Solve complex problems using mechanistic and statistical approaches.
- Develop predictive models for commodity demand, supply forecasting, ETA predictions, and delay detection.
- Optimize logistics simulations and market algorithms to improve operational efficiency.
- Other duties as assigned.
What we're looking for
- Proven track record of independently delivering complex software engineering projects.
- Experience with ML frameworks such as TensorFlow, PyTorch, or Keras.
- Solid background in algorithms, optimization, neural networks, and reinforcement learning.
- Advanced degree (MS preferred) in a quantitative field such as Computer Science or Engineering.