Senior AI Engineer Georgia

Workstream is a mission-driven company building the all-in-one HR, payroll, and hiring platform for managing the hourly workforce. There are 2.7 billion hourly workers, making up 80% of the global workforce, but this market has been heavily underserved by technology and deserves better. Workstream has been purpose-built for the hourly workforce from day one so that these businesses and their employees can thrive.

Our customers include leading brands from multiple sectors, including Burger King, Carl's Jr./Hardee's, IHOP, KFC, and Culvers. We are a high growth series B company and quickly expanding our product portfolio to deliver on our vision. We are backed by legendary VCs and industry experts like Founders Fund, BOND, and Coatue.

Workstream is building AI-native services that deliver completed business outcomes—mature service categories businesses already pay for, delivered by AI agents plus a small human review layer at materially better margins than the traditional alternative. We are validating the first service now with design customers from our existing base.

This is a founding engineer opportunity. As the first engineer on Workstream's AI-Native Services (AINS) team, you'll spearhead a new company initiative from zero to one. You'll shape its architecture, set its engineering bar, and help build the team that follows. This is a Staff level role with a direct line to leadership and real influence over how AINS grows. You will not own pricing, deal acceptance, or the margin model—those sit with the Head of Delivery and the Head of GTM.

You will own the components: whether they work, how they fail, and how fast bespoke work becomes reusable.

This is a full-time, remote position with occasional in-office visits as needed to support close cross-functional collaboration and key team initiatives.

• Build and operate production agents and agentic workflows: orchestration, tool use, data connections, deterministic logic, state, and permissions.
• Work forward-deployed with design customers—sit in their workflow, map the messy reality, and build against live data.
• Build the evaluation harness: eval datasets from real customer work, pre-launch thresholds, regression suites, and post-launch monitoring.
• Build the human-review layer: exception queues, review tooling, escalation paths, and the tracing that makes every agent decision auditable.
• Run shadow-mode parallel runs against the customer's existing process or provider and drive discrepancies to zero before go-live.
• Turn customer-specific work into reusable agents, integrations, and playbooks—productization is the default, customization the exception.
• Handle production incidents end to end, and feed every failure back into evals and controls so it cannot recur.
• Help set the technical bar as the team grows.

• You have personally built and operated at least one AI agent or agentic workflow in production. You can clearly explain how it worked, where it failed, how you evaluated it, and what you kept deterministic or human-led.
• Strong engineering judgment across models, orchestration, APIs, data and integrations, permissions, observability, and evaluations.
• You have worked directly with customers or end users—discovery, implementation, or incident resolution—and communicate clearly with non-technical people.
• You can break a messy workflow into deterministic steps, agentic decisions, human judgment, and exception paths—and define how each part will be tested.
• You care about keeping plausible but wrong AI output away from customers: grounded context, constrained actions, verification, evals, review, monitoring.
• You thrive in zero-to-one ambiguity: shipping weekly, owning outcomes, and doing whatever the service needs—including…