Lead and coordinate AI model enablement and agentic development initiatives across engineering, security, data science, legal, and IT to drive adoption and improve program management processes.
Compensation
The base salary range for this position for all U.S. candidates is $85,000 - $120,000 per year, with eligibility for bonuses, equity grants and a comprehensive benefits package
Responsibilities
Lead end-to-end execution of TechOps AI initiatives
Plan and coordinate model access, evaluation, and approval processes
Align cross-functional teams and act as bridge between technical and non-technical stakeholders
Own status reporting and communication for small to mid-size projects
Drive adoption of new AI-assisted and agentic ways of working
Senior AI Engineer, Hivemind Learning & Development is a hands‑on individual contributor who will build the AI‑powered products, content systems, and measurement capabilities that help engineers learn Hivemind faster and apply it successfully.
Compensation
Pay within range listed + Bonus + Benefits + Equity
Responsibilities
Develop an AI‑powered training coach that helps people learn Hivemind concepts, products, and development workflows.
Create highly technical Hivemind training materials and hands‑on workshops for engineers and other technical learners.
Build prompt‑based tooling that generates workshops and supporting learning content from defined objectives, audiences, and source materials.
Create LLM‑based tooling that automates the ongoing upgrade and improvement of Hivemind training and educational materials.
Build AI tools and systems that enable, streamline, and automate Hivemind Learning & Development workflows.
Own the backlog for one or two AI or data use cases, run use‑case intake, act as daily interface between business sponsor and engineering and data teams, prepare governance submissions, define and track adoption and value KPIs, support vendor and platform evaluations.
Responsibilities
Own the backlog for one or two AI or data use cases
Run use‑case intake
Act as the daily interface between the business sponsor and the engineering and data teams
Lead discovery with the customer: orient in their codebases and workflows, find the real bottleneck and the use case worth building, and define clear success metrics
Design and ship production agents, AI applications, and coding-agent workflows on Grok and our models, on live business processes and inside the customer’s engineering org
Get a first version live in days, then harden it over weeks with rollout, monitoring, and iteration from real usage
Own production quality: tracing, evals, debugging model or delivery failures, and latency and cost tradeoffs
Build the systems around the model (tools, MCP servers, retrieval, rules, skills, CI gates, and evals) and integrate them into the customer’s auth, data, workflow, and delivery systems; decide when prompting, architecture, data, process, or a model change is the right lever
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