AI Engineer
Builds, fine-tunes, and deploys multimodal AI models and agents for trucking logistics automation. Owns full ML lifecycle including data flywheels, evaluation, production adaptation, using Python, TypeScript, and leading AI APIs.
About the job
Responsibilities
- Training, fine-tuning, and deploying state-of-the-art multimodal models across a range of real-world tasks
- Designing and implementing evaluation frameworks to rigorously measure model performance and guide iteration
- Building and scaling data flywheels - collecting, curating, and generating high-quality datasets to continuously improve model outcomes
- Developing systems for live learning, feedback incorporation, and continuous model adaptation in production
- Implementing techniques like negative mining to harden models against edge cases and failure modes
- Owning the full lifecycle from experimentation → validation → deployment → monitoring
- Collaborating closely across engineering and product to integrate models into reliable, high-performance systems
Requirements
- Hands-on experience working with modern foundation models (LLMs, multimodal models, or similar) in production settings
- Strong intuition for model behavior, evaluation, and failure modes
- Experience with fine-tuning, training pipelines, and dataset construction
- Familiarity with techniques like RLHF, synthetic data generation, or active learning (not required, but highly relevant)
- Comfort working across Python-based ML stacks and Typescript-based production systems
- A bias toward action - you run experiments, measure results, and iterate quickly
- The mindset of an owner: you care about outcomes, not just outputs, and push systems to actually work in the real world
Tech Stack
- Next.js, GraphQL, Node, OpenAI, Anthropic
Skills
LLMs, Multimodal Models, Fine-Tuning, RLHF, Synthetic Data Generation, Active Learning, Python, TypeScript, Next.js, GraphQL, OpenAI, Anthropic
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