Staff AI Enablement Engineer

BetterHelp

4d•$170,000 - $215,000•Remote

About The Position

BetterHelp is the world's largest online therapy service, aiming to make mental health care more accessible. As the Staff AI Enablement Engineer, you will join a diverse team focused on expanding access to therapy. This role is a first-of-its-kind at BetterHelp, focused on making the engineering organization more effective with AI. You will be responsible for driving the adoption of AI tools and initiatives within the engineering, product, and design teams, with developers, data, and devops as initial customers. This is a senior individual contributor role on the Platform team, reporting to the Platform's engineering manager, who owns the AI enablement roadmap. You will have significant autonomy in how the work is built and accountability for its adoption.

Requirements

6+ years in fast-moving technology organizations, including hands-on software engineering (platform engineering, developer experience, internal tools, or infrastructure).
Daily, hands-on fluency with the modern AI stack: coding agents (e.g. Claude Code, Cursor), MCP servers and connectors, prompt and context engineering, retrieval-augmented generation, and workflow automation platforms.
Demonstrable infrastructure competence: infrastructure as code (Terraform or equivalent), a major cloud provider, and secrets and access management.
Comfort working inside CI/CD, with the ability to reason about pipeline cost, latency, and failure modes.
Demonstrated change management across a medium to large organization, including championing programs, creating enablement content, conducting training, and establishing an operating rhythm to maintain momentum.
Governance fluency: data privacy, safe-use policy, vendor evaluation, and access control, with the judgment to move fast within a regulated environment.
A self-starter who operates with autonomy and knows when to escalate to get unblocked.

Responsibilities

Build against the AI enablement roadmap for the engineering, product, and design org, determining what to adopt, what to build in-house, the order of implementation, and how to measure success.
Turn the roadmap into working software, taking ownership of the implementation.
Build the internal AI stack hands-on, including AI agents and automations for recurring engineering workflows, MCP connectors into systems of record, shared prompt and skill libraries, and a governed knowledge layer for company context.
Own experiments end-to-end, including setting up separate AWS environments, using Terraform for infrastructure management, and deploying and maintaining secure solutions.
Ship experiments, prioritizing those that demonstrate success and doubling down on effective solutions.