Engineering Manager, Benepass

• This is not a “nice to have” and it is not a mandate you pass down while you keep working the old way. We want a manager who already heavily leverages AI in their own work, every week, across the job — coding, debugging, design, planning, writing, analysis, 1:1 prep, hiring, and communication
• Across Engineering, most new code is already written with AI in the loop. That is the baseline, not a future state. The managers who succeed here treat that as leverage: they get to higher-quality judgment faster, they automate the repetitive parts of the job, and they raise the team’s standard instead of lowering it
• You use it yourself, daily. You have a real workflow — not “we bought Copilot.” You can walk through how you actually design, debug, write, and review with AI. You have changed how you work, not bolted a chatbot onto old habits
• You hold the same bar. AI-assisted work is held to the same standard of correctness, security, and craft as everything else. On Core Services that is non-negotiable: ledger, decisioning, and money-movement mistakes are real. You review output. You do not outsource judgment. People own outcomes; AI does not
• You increase leverage, not ceremony. You look for work to automate: tests, runbooks, design-doc first drafts, incident timelines, onboarding, repetitive ops. You kill toil. You do not add an “AI process.”
• You coach the team in the craft. Learning to work effectively with AI is now part of engineering. You set the expectation, share what works, and help people who are behind catch up — without turning it into theater or a purity test for one vendor
• You are curious and unsentimental. Tools change. You evaluate new ones the same way you evaluate anything else: does this make us more effective? You experiment in the open, keep what compounds, drop what doesn’t
• You do not hide behind it. AI is not an excuse for sloppy PRs, missing context, or skipped thinking. If the model is wrong, you still own the result
• Technical Expertise: Deep software engineering experience in Python/Django (or equivalent backend systems) so you can guide architecture, review PRs, and contribute code when it matters. You think in systems, not endpoints
• Leadership & Empathy: Exceptional communicator who builds high trust and high accountability. You create psychological safety, coach engineers toward growth goals, and do not hide problems or wait for someone else to own them
• Outcome-Driven: You own end-to-end results — from sequencing work to production health. The business experiences outcomes, not tickets
• Disciplined Craft: You fight complexity. Clean code, tests, observability, and small building blocks. Principles over rules; judgment over bureaucracy
• AI-Native: You heavily use AI yourself. This is a personal practice, not a talking point. You can show how it has changed your week: how you write and review code, how you debug, how you prepare for 1:1s and design reviews, how you draft and tighten communication. You are not waiting for a platform team to hand you a workflow. You hold AI-assisted work to the same bar as everything else, and you can teach that to a team without turning it into process
• Experience: 5+ years of software engineering, including 2+ years leading teams of ~3–8 engineers (or equivalent scope), ideally in a startup or SaaS environment
• Technical Leadership: Proven ability to lead design reviews, enforce engineering standards, and prototype in Python/Django (or a comparable backend stack). Comfortable on systems where correctness and reliability are not optional
• People Development: Experience mentoring engineers through 1:1s, setting growth goals against a leveling matrix, handling performance issues directly, and hiring
• Delivery Excellence: Track record of shipping on commitments, creating clarity from ambiguity, and running a healthy SDLC without turning process into the product
• AI as a personal craft: You already use AI heavily in your own work — coding, debugging, planning, writing, and operating a team. You can describe your workflow in concrete terms. You have used it to increase your (and a team’s) effectiveness, not as a demo. You review and own the output. Candidates who “support AI adoption” but barely use it themselves are not a fit

Desirable

• As an Engineering Manager (M3) at Benepass, you will lead a team of backend engineers on Core Services — the systems that sit on our critical path: the ledger, decisioning, schedules, rules engine, HRIS integrations, authentication, and directory
• These are the brains and books of the platform
• Every swipe, payout, credit, and adjustment flows through them
• You will report to the Vice President of Engineering and split Core Services with a peer Engineering Manager
• This is how we scale leadership without turning one manager into a bottleneck
• You will own people, execution, and outcomes for your slice of the team
• Engineers stay anchored to Core Services for growth and accountability; they ship through time-boxed projects with Product, Design, and Frontend Engineering
• This is a hands-on EM role
• You have a strong technical background, stay close enough to the systems to review designs, spike prototypes, and debug when it matters, and you do not try to be the best IC in the room
• You create the environment — clarity, staffing, and culture — so ICs can own architecture and delivery
• Managers own team execution
• Project leads own project outcomes
• Engineers own the quality and impact of their work
• This role is a culture-carrier for how Engineering works
• We hire managers who already operate this way — not people we have to convince
• Manage ~4–6 engineers to start, with room to hire as we grow
• Run 1:1s that are about growth, not status. Set expectations against our IC leveling matrix. Give feedback early, including the hard kind
• Hire, onboard, and level the team. Design a hiring bar you would be proud to join
• Coach project leads. They drive kickoff, technical approach, weekly updates, and release — you make sure they have the support and air cover to do it well
• Be accountable for results, not activity. Stay engaged until the problem is solved, not when the ticket closes
• Translate company priorities into a sequenced plan the rest of the company can plan around. If a timeline becomes unrealistic, raise it, cut scope, and make the tradeoff explicit — do not quietly slip it
• Protect operational load. Core Services carries production health, incidents, and correctness work that is not always a roadmap project. Plan for that instead of pretending every hour is feature work
• Incidents take priority over roadmap. Escalate risks early
• Lead design reviews. Champion simple, correct, observable patterns. Leave systems better than you found them
• Jump into code, prototypes, or production debugging when it unblocks the team or validates a hard tradeoff — then step back
• Hold the line on invariants that matter for money and eligibility: idempotency, auditability, reconciliation, and safe retries. Prefer a useful v1 over a perfect design debate
• Partner with Client Apps, Platform, Customer Engineering, Product, and Ops so Core Services is easy to build on — not a bottleneck wrapped in tribal knowledge
• Share context early. Document decisions. Surface risks before they become problems. Ask for feedback while the work is still easy to change
• Project teams are empowered to decide and expected to drive. Ownership includes gathering context, making tradeoffs, and unblocking yourself when you can
• OUR TECHNOLOGY AND STACK
• Python, Django, Django Rest Framework, PostgreSQL, AWS, Docker — plus the adjacent client surfaces (React / TypeScript) your APIs power