Lead AI Systems Engineer

3 нед. назад

USALeadRemote

Lead AI Systems Engineer role at , working on AI-driven fintech platform for customizable benefits.

О компании

At 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 , 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.
Helping companies reimagine how companies take care of their people.
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.

Требования

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.

Условия

Base salary of $190,000 to $220,000 + equity.
95% coverage of medical, dental, and vision
Fantastic benefits (of course 😃), including: $250 WFH setup (one time)
$500/year Learning & Development Benefit
$150/month cell phone + internet
$100/month Wellness
$100/month Co-working and Commuter Benefit
We offer several team onsites a year
Flexible PTO
At , we are working towards reimagining how companies take care of their people. We are committed to creating an inclusive environment for all our employees and are seeking to build a team that reflects the diversity of the people we hope to serve with our revolutionary products. is proud to be an equal-opportunity employer.

Другое

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.
Founder Story - Jaclyn Chen
Raises $40M Series B
| Candidate Resource Page
Listed on Inc. Magazine's Best Workplaces of 2023
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 ’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 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 move faster, with higher confidence, and with less toil—first in Engineering, then across the company.
Own the design and implementation of ’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 .
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.
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 deliberately.
Help adopt AI in a way that reduces toil, increases speed and coverage, and keeps humans firmly in control of quality and production outcomes.
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.