Building infrastructure to run AI faster, efficiently, and on your own terms.
AI Field Engineer
Generative models are powerful, but deploying them reliably without bleeding compute budgets remains a massive headache. As an AI Field Engineer, you will bridge the gap between cutting-edge inference infrastructure and the engineers building on top of it, diving directly into customer environments to optimize model serving on Kubernetes and major cloud platforms. You will architect solutions, untangle complex integration bottlenecks, and ensure AI natives across finance and healthcare get maximum performance from their deployments.
What they're looking for
- 3 to 10 years of experience in solutions, forward deployed, or machine learning engineering.
- Deep proficiency in Python, with strong practical knowledge of Kubernetes orchestration.
- Hands-on expertise deploying infrastructure across AWS, Azure, or GCP.
- Proven ability to translate customer pain points into robust, scalable technical architectures.
- Willingness to operate in a growth-stage environment, embedded directly with external teams.