Lead AI Systems Engineer, Benepass
$190-220k
+ Equity
TypeScript
JavaScript
Python
Senior and Expert level
Remote from US
Benefits administration platform
Be an early applicant
Benefits administration platform
21-100 employees
B2BHRPaymentsSaaS
Be an early applicant
$190-220k
+ Equity
TypeScript
JavaScript
Python
Senior and Expert level
Remote from US
21-100 employees
B2BHRPaymentsSaaS
Company mission
To help companies distribute meaningful benefits that support the personal and professional well-being of their team.
Role
Who you are
- You’ve shipped end-to-end AI systems—agents, tools, retrieval, evals, integrations—not demos or notebooks
- You blend off-the-shelf tools (e.g. Cursor-class agents) with custom platform where it compounds
- You’re AI-native in your own workflow and clear-eyed about where the tools break
- Biased to shipped leverage over endless POCs; honest about AI failure modes
- Believes AI should augment people and raise quality bars, not replace judgment
- Proven staff or equivalent cross-functional/platform impact; you still write code
- You turn ambiguous 0→1 spaces into sequenced roadmaps with real adoption metrics
- Background in full-stack, developer tools, and/or infra automation—you build paved roads teams actually use
- You embed selectively, prove value, then productize patterns onto a shared platform
- You partner across Engineering, Product, Design, and Operations without becoming a bottleneck
- You measure success by adoption and outcomes, not tool count or novelty
- Staff-level (or equivalent platform/cross-functional) impact shipping systems others depend on
- Strong programming fundamentals (Python and/or TypeScript/JavaScript preferred)
- Experience building internal platforms, developer tools, or automation integrated into CI/CD
- Hands-on with modern AI systems: coding agents, LLM APIs, orchestration, and production operability
- Experience enabling quality through automation (E2E/integration testing, intelligent quality gates)
- Solid systems instincts: APIs, permissions, observability, reliability
- Clear communicator who can set standards and drive multi-team adoption
- Bonus: agentic workflows, eval harnesses, RAG/knowledge systems, or ops/workflow automation beyond eng
- Bonus: fintech/regulated domains, or 0→1 platform ownership at a growth-stage company
What the job involves
- 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
- 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
- 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
- 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
- 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
- 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
- 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 tooling, and bring the best of the ecosystem into Benepass deliberately
- Help Benepass adopt AI in a way that reduces toil, increases speed and coverage, and keeps humans firmly in control of quality and production outcomes
Our take
The US labor market is creating higher demand for top tier talent, and many companies are looking to improve their benefits as a way of staying competitive. Strong health insurance and holiday policies, however, no longer cut it. Benepass is a benefits administration company looking to help companies stay ahead in this new landscape.
The basic Benepass premise is that pre- and post- tax benefits should be flexible and accessible for employees. As things stand, tax-advantage benefits are hugely under-leveraged by employees, because they are hard to use or unsuitable for individuals. Benepass’ app centralises and simplifies the process, allowing employees to explore, track, and exercise benefits from a single platform and VISA card.
Many platforms directed towards simplifying employee benefits don’t yet offer the same tangible features, and also don't focus on the more complex area of tax-advantage benefits. Therefore, Benepass’ solution is an easy win/win, which is why it has gained strong funding and attention. The latest influx fuels the expansion of its offerings, for example to support more specialized and cost-sensitive employer programs.
Freddie
Company Specialist at Welcome to the Jungle
Insights
Led by a woman
Top investors
8% employee growth in 12 months
Company
Funding (last 2 of 5 rounds)
Jan 2026
$40m
SERIES B
Jan 2024
$20m
EARLY VC
Total funding: $74.4m
Company benefits
- Healthcare: We pay $430 per month toward your health insurance and fully cover dental and vision. You also get membership to OneMedical, Teladoc and Talkspace
- Flexible, autonomous work: We’re remote first so live where you’re most happy. We come together in-person as a team 3-4 times per year
- Benepass perks: $500 to set up your home office, $100 per month for wellness, however you define it, $150 per month for home internet and cell phone, $150 per month for co-working
Company HQ
Greenwich Village, New York, NY
Leadership
Jaclyn Chen
(CEO & Co-founder)Previously an Associate at Goldman Sachs, then TPG Capital, before moving on to work in Business Development at Teampay.
Mark Fischer
(Product & Co-founder)An ex-Google Mechanical Engineer, who went on to hold a Senior Robotics Engineering role at X: The Moonshot Factory.
Kabir Soorya
(CTO & Co-founder)An In-Q-Tel technologist who then worked on the Senior Technical Staff at Sidewalk Labs.
Share this job
View 8 more jobs at Benepass