AI Inference Engineer

Baseten Baseten · Data AI · San Francisco, CA · EPD

Baseten is seeking an AI Inference Engineer to partner with customers, architecting, building, and deploying high-scale production AI applications on their platform. This role involves owning the customer journey from exploration to production, translating business goals into reliable, observable services with clear quality, latency, and cost outcomes. Responsibilities include developing software systems, driving customer impact through end-to-end solution implementation, optimizing AI/ML projects, and functioning as an engineer, project manager, and product manager.

What you'd actually do

  1. Develop and maintain software systems and product features using one or more general-purpose programming languages in a production-level environment, with a preference for Python due to its relevance in ML projects.
  2. Drive customer impact by designing, implementing, and deploying Baseten solutions end-to-end (problem framing → evaluation → production deployment → monitoring). This involves working with customers’ engineering teams at every stage of the customer journey including: sales, implementation, and expansion.
  3. Deliver with velocity: turn vague objectives into clear specs and well-defined PoCs so we can rapidly ship well-tested services and outcomes for our customers
  4. Optimize and enhance AI/ML projects, contributing to the continuous improvement of our technical stack. This includes developing features and PRDs with other engineering and product orgs.
  5. Own products and customer projects end-to-end, functioning as both an engineer, project manager, and product manager, with a focus on user empathy, project specification, and end-to-end execution.

Skills

Required

  • Python
  • software development
  • AI/ML pipelines
  • ML model development and deployment
  • communication skills

Nice to have

  • experience in building or optimizing AI/ML projects

What the JD emphasized

  • high-scale production AI applications
  • production deployment
  • production-level environment
  • AI/ML projects

Other signals

  • customer-facing AI applications
  • high-scale production inference
  • deploying models into production
  • optimizing AI/ML projects