Note: The job is a remote job and is open to candidates in USA. Benepass is a company focused on making benefits easy for organizations through a customizable fintech platform. They are seeking a Lead AI Systems Engineer to build their internal AI platform, which will enhance operational efficiency and automate workflows across various teams.
Responsibilities
- 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
Skills
- 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
- Agentic workflows, eval harnesses, RAG/knowledge systems, or ops/workflow automation beyond eng
- Fintech/regulated domains, or 0→1 platform ownership at a growth-stage company
Benefits
- 95% coverage of medical, dental, and vision
- $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
Company Overview