Skip to main content

NimbleRx

AI-Forward Engineer

Redwood City, CA$152k–$285kfull-timemidAdded 1 month ago

About this role

As an AI-Forward Engineer at Swoop, you'll lead the charge in integrating AI-powered tools that enhance various teams across the organization, from product to marketing. This role requires a strong foundation in software engineering and a keen interest in the evolving AI landscape to drive innovation in healthcare marketing.

What you'll do

  • Experiment with and evaluate AI tools and workflows
  • Integrate AI solutions across teams
  • Rapidly prototype new AI-driven products
  • Collaborate closely with various departments
  • Drive innovation and efficiency in the organization
  • Stay updated on AI advancements

What they're looking for

  • Solid software engineering fundamentals
  • Deep curiosity about AI ecosystem
  • Strong collaboration skills
  • Prototyping and integration expertise
  • Adaptability to evolving technologies
  • Problem-solving abilities
  • Understanding of healthcare marketing
  • Team-oriented mindset

Benefits

  • Opportunities for professional growth
  • Innovative work culture
  • Recognition as a top workplace
  • Patient-first philosophy
  • Fast-paced and dynamic environment
  • Impactful work in healthcare
Apply with Autofill

Opens the application — the Jobs AI extension fills it for you. Set up autofill

Opens the official application on the employer’s site. No login required.

NimbleRx

NimbleRx develops AI-powered tools designed to enhance healthcare marketing and organizational operations. The company is hiring AI-Forward Engineers who can integrate and implement AI solutions across product, marketing, and other business functions.

View all jobs at NimbleRx

Likely interview questions

  • Tell us about a time you evaluated and chose between multiple AI tools or models for a specific problem. How did you measure which one was the best fit?
  • Describe your experience prototyping with AI products quickly. How do you balance speed with technical rigor when experimenting?