Lead AI Systems Engineer

About Us

At Benepass we're making benefits easy. We believe people are the most important asset to any company. Traditional one-size-fits-all benefits packages no longer cut it in today's hybrid and remote-first environment. With Benepass, companies can tailor their benefits to the unique needs of their workforce.

Through our easy-to-use and highly customizable fintech platform, People teams can implement, administer, and track the benefits that meet employees where they are. Employers design their benefits and perks plan by setting a contribution amount and eligible spend categories. Every employee has their own individual definition of wellness and needs different things to help them be their most productive, fulfilled self.

Our Mission

Helping companies reimagine how companies take care of their people.

Our Investors

We are backed by leading investors, including Centana Growth Partners, Portage Ventures, Threshold Ventures, Gradient Ventures, Workday Ventures, and Clocktower Technology Ventures. To date, the company has raised approximately $75 million in equity capital.

Articles

Founder Story - Jaclyn Chen

Benepass Raises $40M Series B

Candidate Resources

Benepass | Candidate Resource Page

Benepass Listed on Inc. Magazine's Best Workplaces of 2023

TEAM & ROLE

Benepass is scaling quickly, and the complexity of our product, engineering, and operational surface demands a modern, intelligence-driven approach to how work gets done. We are looking for a Lead AI Systems Engineer (P5) to build our internal AI platform from the ground up; from 0→1 foundation-building to 1→2 maturity and scale.

This role is deeply technical, highly strategic, and critical to Benepass’s velocity. You will own the AI systems roadmap and architect a shared internal AI platform that starts with the engineering SDLC, and is explicitly designed to scale into automation and agentic systems across the company (Operations, Product, Design, Support, and other internal workflows).

You’ll sit as a Staff IC in Platform Engineering , owning the platform while selectively embedding with teams to drive real adoption. Engineering is the first beachhead: coding assistants, CI/CD, quality automation, and knowledge systems. The platform primitives you build—agents, tools, retrieval, evaluations, permissions, and workflow runners—should generalize beyond Engineering so Benepass can automate high-leverage work company-wide without reinventing the stack each time.

This is a force-multiplier role. Near-term success looks like faster PR and cycle times, higher AI-tool adoption, faster and more reliable automated testing, better knowledge findability, and a measurably better developer experience. Longer-term success looks like the same platform powering durable automation outside the SDLC—SOPs, operational workflows, cross-functional knowledge, and internal systems—with clear ROI and adoption.

This is not a research/data-science role, a customer-facing product-ML role, a prompt-only chatbot job, or a manual process-ownership seat. This role is for a Staff-level technical leader who builds full-stack AI systems end-to-end , with strong platform, developer-tools, and infrastructure instincts. Your mission is to build the AI-powered guardrails and accelerators that let Benepass move faster, with higher confidence, and with less toil—first in Engineering, then across the company.

YOU WILL

Build Benepass’s Shared AI Platform (0→1 and 1→2)

Own the design and implementation of Benepass’s internal AI platform and strategy— engineered for company-wide leverage , not an Engineering-only toolchain.

Use the engineering SDLC as the first beachhead, while designing platform primitives that extend cleanly to Operations and other internal functions.

Stand up a pragmatic platform that combines industry-leading tools (e.g., Cursor and peers) with in-house systems, integrations, and shared infrastructure.

Define the architecture for how models, tools, context, evaluations, secrets, and permissions are composed safely inside Benepass.

Build reusable primitives: agents, tool interfaces, retrieval/context layers, workflow runners, observability, and feedback loops.

Establish the foundation for reliable environments, secure access to internal systems and data, and patterns that new domains can adopt without a ground-up rebuild.

Accelerate the Engineering SDLC with AI

Drive AI-assisted coding, review, and delivery workflows that compress time from idea → PR → production.

Integrate AI into CI/CD so quality signals, summaries, risk checks, and developer feedback show up where engineers already work.

Identify SDLC bottlenecks (local dev, code review, test wait time, release friction, knowledge gaps) and remove them with automation.

Measure what matters: PR/cycle time, adoption, developer satisfaction/DX, test speed & signal, and time-to-find knowledge.

Turn successful team-level experiments into platform defaults that scale across Engineering—and inform patterns for non-eng domains.

Enable AI-Driven Quality, Testing, and Release Confidence

Own the strategy for AI-driven test generation, maintenance, and automation—especially where it unlocks broad end-to-end coverage.

Build systems that help engineers own quality: high-signal E2E coverage, faster feedback, lower flakiness, and less manual validation.

Partner with Platform and product teams to put intelligent quality gates into CI/CD and deployment workflows.

Use AI to improve regression detection, failure triage, and the loop from requirements → test plan → execution → root-cause analysis.

Create clarity on ownership: what quality belongs to every engineer vs. what the AI/platform layer provides as shared leverage.

Build Agentic Workflows and Knowledge Systems (Eng → Company-Wide)

Design and ship agentic internal workflows that automate multi-step work, not just single-prompt assistants; starting in Engineering and expanding to other teams.

Build knowledge/search systems so people and agents can find specs, decisions, runbooks, SOPs, and operational context quickly.

Connect agents to the systems teams already use (repos, CI, docs, issue trackers, ops tools, internal dashboards) with clear permissions and auditability.

Prioritize workflows with obvious ROI: repetitive operational toil, cross-repo changes, test authoring, incident/context gathering, onboarding, and cross-functional SOPs.

Ensure these systems are observable, evaluable, and maintainable—and that the same platform can host automation outside the eng SDLC without forking the architecture.

Partner Across the Company as a Platform Force Multiplier

Operate as a Staff IC on Platform: set direction, build the core, and embed selectively where adoption and design feedback matter most.

Collaborate with engineers early so systems are testable, scriptable, and easy to integrate into existing workflows; then apply the same enablement model with non-eng partners.

Work with Engineering, Product, Design, Operations, and other leaders to choose the journeys and workflows worth automating first.

Provide documentation, reference implementations, guardrails, and golden paths so teams can adopt without heroics.

Raise the organizational bar for what “good” looks like in AI-assisted work. Software delivery first, then broader internal automation.

Define Standards, Evaluations, and Responsible Adoption

Introduce standards for AI tool usage, prompt/tool patterns, evaluation, data handling, and human-in-the-loop controls that work across Engineering and other internal domains.

Build evaluation harnesses and quality metrics so we know when AI systems are helping—and when they are creating noise.

Make pragmatic build-vs-buy decisions, favoring speed and leverage while investing in shared platform where it compounds company-wide.

Stay current on emerging coding agents, workflow agents, eval methods, and enterprise AI