Staff AI Engineer — Embedded Production Workflows

hims-and-hers; United States; Hybrid; USD 180,000 - 240,000; Full time

Benefits offered by this job

Competitive salary

Unlimited PTO

Health benefits

ESPP

401k matching

Offsite retreats

Job summary

Hims & Hers is seeking a Staff Forward Deployed Engineer to push AI-powered workflows into production across business teams. You will embed with a function, map current processes, and identify where AI adds the most value, then ship end-to-end solutions.

This role requires deep software engineering experience, strong Python and backend skills, cloud familiarity, and the ability to translate technical concepts for business partners while delivering reusable platform capabilities at scale.

Qualifications

• You have 8+ years of professional software engineering experience.
• You demonstrate strong Python and backend or full-stack capabilities.
• You possess cloud experience (AWS, containers) and CI/CD workflows.
• You have working knowledge of LLMs and agentic patterns.

Responsibilities

• Embed with a team to understand current workflows and needs.
• Map the function's work and surface bottlenecks and manual steps.
• Identify AI opportunities and align with the team on priorities.
• Build production-grade AI workflows that scale across the company.
• Deliver reusable capabilities and measure business impact.
• Own outcomes by tracking revenue, costs, and cycle-time improvements.

Skills

Python

Backend

AWS

Kubernetes

CI/CD

LangChain

LLMs

Tools

React

TypeScript

Job description

Hims & Hers is seeking a Staff Forward Deployed Engineer to push AI-powered workflows into production across business teams. You will embed with a function, map current processes, and identify where AI adds the most value, then ship end-to-end solutions.

This role requires deep software engineering experience, strong Python and backend skills, cloud familiarity, and the ability to translate technical concepts for business partners while delivering reusable platform capabilities at scale.