The Role & Your Charter
What you'd actually own on Cursor's Infrastructure team
The mandate: the layer every service runs on
After this you can articulate the four pillars of the Infrastructure team's charter.
You'd own the floor that everything else at Cursor stands on. When a product engineer ships a service, your platform is what carries the packet, schedules the container, terminates the TLS and pays the cloud bill.
Read this section as the role contract. The diagram or table names the surface area, but the interview signal is whether you can turn it into a clear operating claim: what you own, what you do not own, what evidence proves the work is working and where judgment matters.
The JD reads as a list of cloud nouns, but it resolves into four pillars. Learn them as a single mental map so that when an interviewer asks "what would you own here?" you answer in structure, not in keywords.
The AWS VPC layer underneath every service: subnets, route tables, NAT, peering vs. Transit Gateway, PrivateLinkAn AWS feature that keeps traffic to a service on your private network instead of the public internet..
Security groups and NACLs, private connectivity between services and to data stores.
Get this wrong and nothing above it routes; get it right and no one notices it exists.
Production EKS/Kubernetes: scheduler behavior, the control plane, ingress controllers, the CNI.
Autoscaling that reacts to the right signal (HPA/VPA, Cluster Autoscaler or Karpenter).
A service mesh for mTLS, traffic splitting, retries and per-hop observability.
CDN, WAF, TLS termination and Anycast at the front door.
Rate limiting and abuse mitigation so one bad actor can't degrade 1M+ users.
Traffic routing decisions: who gets sent where and why.
The compute that actually runs the product, unified under one opinionated platform.
Image supply chain, build/deploy pipelines, GitOps rollout.
Cost attribution woven through all of it, because every pillar spends money.
Interactive diagram. Tab through its regions; each focused region shows its detail in the panel below.
Read bottom-up: the network carries the packet before anything above it can route.
These are platform responsibilities, not ops ticketswho your customers are
Your users are other Cursor engineers. They don't file a request and wait; they git push and expect a reliable deploy to come out the other side. That framing changes everything about how you describe the work.
Think paved roads and opinionated defaults, not ticket-taking. You ship guardrails and golden paths so the easy way is also the safe, fast, cheap way. A product engineer who never has to think about your layer is the success metric.
Where one team runs ops as a queue of requests, the strong infra org runs it as a product with internal customers, a roadmap and a usability bar. You measure adoption of your paved road, not tickets closed.
Know your boundaries with adjacent teamsso you can say where this role stops
Interviewers probe whether you understand the seams. Cursor's exact org chart isn't public, but most fast-growing product companies split infra responsibilities along recognizable lines. Speak to the boundary and flag any specific claim as your inference.
- Adjacent team
- ML / Model Infrastructure
- Owns
- GPU fleets, inference serving, model training and rollout pipelines
- Where you hand off
- You provide the network, IAM and cost substrate; they own the inference runtime on top
- Adjacent team
- Core / Backend Services
- Owns
- Application services, APIs, business logic
- Where you hand off
- They consume your clusters, mesh and deploy pipeline; you don't own their code
- Adjacent team
- Billing / Platform Data
- Owns
- Usage metering and customer billing
- Where you hand off
- You feed cloud-cost and tagging data; they turn customer usage into invoices
| Adjacent team | Owns | Where you hand off |
|---|---|---|
| ML / Model Infrastructure | GPU fleets, inference serving, model training and rollout pipelines | You provide the network, IAM and cost substrate; they own the inference runtime on top |
| Core / Backend Services | Application services, APIs, business logic | They consume your clusters, mesh and deploy pipeline; you don't own their code |
| Billing / Platform Data | Usage metering and customer billing | You feed cloud-cost and tagging data; they turn customer usage into invoices |
Framing only - treat exact team names as illustrative unless you can confirm them. The seam, not the org chart, is what the interviewer is testing.
When asked to describe the role, answer in the four pillars and then name one boundary unprompted: "I'd own network, orchestration, edge and compute - and I'd expect ML Infra to own GPU serving on top of the substrate I provide." Naming the seam unprompted signals you think in systems, not silos.
Takeaway. The Infrastructure charter is four pillars - network foundations, container orchestration, edge & security and production compute - run as a platform-as-product whose customers are other Cursor engineers shipping reliably out of the box.
Self-check
QWhich framing best describes how a strong candidate should position the Infrastructure role at Cursor?
Scale and stakes: 1M+ DAUs on a fast-growing tool
After this you can quantify why infra decisions here are first-order business problems.
Cursor serves over 1 million daily active users and is one of the fastest-growing developer tools in the world. At that volume, infrastructure decisions stop being plumbing and start being the business.
Most infra interviews reward correct architecture. This one rewards correct architecture plus a felt sense of consequence. The candidate who says "we'd add a region" sounds junior next to the one who says "a region adds standby cost and data-locality complexity, so I'd justify it against a measured p99 latency budget for that geography."
- Cost
- A 10% efficiency win on a large cloud bill is real money, recurring monthly. Waste compounds; so does a good tagging decision.
- Latency
- Developers feel keystroke-to-completion latency directly. Multi-region routing and tail latency are product quality, not nice-to-haves.
- Abuse
- At 1M+ DAU you are a target. A single unthrottled endpoint can become an outage or a cost spike within minutes.
- Reliability
- 0.1% error rate at this volume is thousands of broken sessions an hour. Tail behavior is visible to a crowd.
The reliability math, made concretewhy 0.1% is not small
Translate percentages into people. It's the fastest way to show an interviewer you reason about scale rather than reciting it.
- Metric
- 99.9% success
- Sounds like
- Three nines, fine
- At ~1M DAU actually means
- ~1,000 affected users per million requests - a steady stream of failures
- Metric
- p99 latency
- Sounds like
- 1% of requests
- At ~1M DAU actually means
- Tens of thousands of sessions per day landing in the slow tail
- Metric
- 1 hour of downtime
- Sounds like
- A blip
- At ~1M DAU actually means
- A visible, talked-about outage for a developer tool people live in all day
- Metric
- One hot endpoint
- Sounds like
- Edge case
- At ~1M DAU actually means
- Abuse or a retry storm that can saturate a service before alerts fire
| Metric | Sounds like | At ~1M DAU actually means |
|---|---|---|
| 99.9% success | Three nines, fine | ~1,000 affected users per million requests - a steady stream of failures |
| p99 latency | 1% of requests | Tens of thousands of sessions per day landing in the slow tail |
| 1 hour of downtime | A blip | A visible, talked-about outage for a developer tool people live in all day |
| One hot endpoint | Edge case | Abuse or a retry storm that can saturate a service before alerts fire |
Percentages hide the headcount. Translate them and the stakes become obvious.
Your work is a growth lever or a growth ceilingthe honest stakes
- Regional strategy decides whether a new market gets fast, local responses or sluggish cross-ocean round trips.
- Cost discipline decides how much runway each dollar of infra buys as usage climbs.
- Platform unification decides whether every new team ships in a day or reinvents deploy from scratch.
- Abuse resistance decides whether growth in users is growth in revenue or growth in attack surface.
Reciting "1 million DAU" as a headline isn't signal. The signal is connecting the number to a tradeoff: "because we're at this scale, I'd spend on a CDN and aggressive caching before adding a third region, since most of the latency win is at the edge." Tie the number to a decision or don't say it.
At a million-plus DAU, a tenth of a percent error rate is thousands of broken sessions an hour and the cloud bill is large enough that right-sizing pays a salary. So I treat cost, tail latency and abuse as product requirements with budgets, not as background hygiene.
Takeaway. At 1M+ DAU, cost, multi-region latency and abuse-resistance are first-order business problems - translate every percentage into affected users and tie the scale number to a specific tradeoff, never quote it on its own.
Self-check
The five concrete responsibilities, decoded
After this you can translate each JD bullet into the systems work it implies.
The JD lists responsibilities at the altitude of a headline. Your job in the loop is to drop each one into the actual systems work it implies, with named technologies and a real decision attached.
Read this section as the role contract. The diagram or table names the surface area, but the interview signal is whether you can turn it into a clear operating claim: what you own, what you do not own, what evidence proves the work is working and where judgment matters.
A candidate who repeats the bullet ("I'd operate Kubernetes") sounds like they read the post. A candidate who decodes it ("I'd run EKS with Karpenter for bin-packing, an ingress controller fronting the mesh and HPA driven by request concurrency, not CPU") sounds like they've done it.
Interactive diagram. Tab through its regions; each focused region shows its detail in the panel below.
Ranked by how central each is to the system-design and deep-dive rounds - prep depth accordingly.
- JD responsibility
- Operate K8s/EKS at scale
- What it actually means
- Production clusters with service mesh, autoscaling and ingress that survive node churn and traffic spikes
- Decision you should be ready to defend
- Karpenter vs. Cluster Autoscaler; sidecar mesh vs. sidecarless; scale signal (concurrency vs. CPU)
- JD responsibility
- Design geo-deployment
- What it actually means
- Multi-region (and possibly multi-cloud) topology for latency and resilience
- Decision you should be ready to defend
- Active-active vs. active-passive; how you handle data locality and failover; the latency budget that justifies a region
- JD responsibility
- Build the edge & security layer
- What it actually means
- Front-door protection: WAF, rate limiting, abuse mitigation, traffic routing
- Decision you should be ready to defend
- Token bucket vs. sliding window; where you throttle (edge vs. service); how you separate abuse from a legitimate spike
- JD responsibility
- Own cost attribution & tradeoffs
- What it actually means
- Tag-based attribution, waste identification, explicit cost-vs-reliability calls
- Decision you should be ready to defend
- Tagging/chargeback model; Spot vs. on-demand for which workloads; where reliability is worth the spend
- JD responsibility
- Unify the compute platform
- What it actually means
- One opinionated orchestration strategy every team deploys onto
- Decision you should be ready to defend
- What to standardize vs. leave flexible; how you migrate teams without breaking them; the paved-road contract
| JD responsibility | What it actually means | Decision you should be ready to defend |
|---|---|---|
| Operate K8s/EKS at scale | Production clusters with service mesh, autoscaling and ingress that survive node churn and traffic spikes | Karpenter vs. Cluster Autoscaler; sidecar mesh vs. sidecarless; scale signal (concurrency vs. CPU) |
| Design geo-deployment | Multi-region (and possibly multi-cloud) topology for latency and resilience | Active-active vs. active-passive; how you handle data locality and failover; the latency budget that justifies a region |
| Build the edge & security layer | Front-door protection: WAF, rate limiting, abuse mitigation, traffic routing | Token bucket vs. sliding window; where you throttle (edge vs. service); how you separate abuse from a legitimate spike |
| Own cost attribution & tradeoffs | Tag-based attribution, waste identification, explicit cost-vs-reliability calls | Tagging/chargeback model; Spot vs. on-demand for which workloads; where reliability is worth the spend |
| Unify the compute platform | One opinionated orchestration strategy every team deploys onto | What to standardize vs. leave flexible; how you migrate teams without breaking them; the paved-road contract |
One row per JD bullet. Memorize the third column - the defensible decision is what separates signal from recitation.
Each responsibility ships a concrete artifactname the deliverable, not the activity
Artifact: a hardened EKS cluster spec plus the autoscaling and ingress config behind it.
Signal: you reason about node lifecycle, disruption budgets and what happens during a rollout.
Artifact: a region topology with a global load-balancing and failover plan.
Signal: you can state the latency budget and the data-consistency model out loud.
Artifact: a rate-limiting and abuse-mitigation policy at the WAF/CDN tier.
Signal: you pick a specific algorithm and say where it runs and why.
Artifact: a tagging/attribution scheme plus a paved-road deploy path.
Signal: you tie a dollar figure to a reliability decision instead of gold-plating.
For any responsibility you're asked about, follow the pattern: name the technology, name the alternative you rejected and name the signal you'd scale on. "Karpenter over Cluster Autoscaler for faster, cheaper bin-packing; I'd scale on request concurrency, not CPU, because the workload is I/O-bound." Three sentences and you sound senior.
Takeaway. Decode every JD bullet into named technology + rejected alternative + the signal you'd scale on - recitation reads junior, a defensible decision reads like someone who has run it in production.
Self-check
QThe JD says "design the networking and security layer at the edge to protect against abuse." What concrete decisions does that bullet actually imply you'd own?
What 'qualified' means here
After this you can self-assess against the required and nice-to-have bar.
The bar is a functioning senior IC who ships from week one. Not a strong junior to be grown, not a manager who architects from a distance - someone who can be handed an ambiguous infra problem and return working, reproducible systems.
Read the requirements honestly against your own history before you read them aspirationally. The loop will find the gap; better that you find it first and decide how to handle it.
- Area
- AWS depth
- Required bar
- VPC networking, EKS/Kubernetes, IAM from real production use
- Nice-to-have
- Multi-cloud breadth
- Area
- K8s at scale
- Required bar
- Operated production clusters with a service mesh and multi-region deployments
- Nice-to-have
- Karpenter, advanced mesh tuning (sidecarless)
- Area
- Edge networking
- Required bar
- CDN/WAF architecture: rate limiting, DDoS/abuse protection, routing
- Nice-to-have
- Anycast and global LB design at scale
- Area
- Infrastructure-as-code
- Required bar
- Terraform/Pulumi with a reproducibility-first mindset
- Nice-to-have
- Progressive/safe rollout of infra changes, drift control
- Area
- SWE fundamentals
- Required bar
- Write real production code (Go/Rust/TS/Python), not just config
- Nice-to-have
- Strong CS fundamentals under a no-AI time box
- Area
- Cost engineering
- Required bar
- Awareness of cost-vs-reliability tradeoffs
- Nice-to-have
- Attribution/chargeback and waste identification at scale
- Area
- Migration
- Required bar
- -
- Nice-to-have
- Infrastructure migration and platform-unification experience
| Area | Required bar | Nice-to-have |
|---|---|---|
| AWS depth | VPC networking, EKS/Kubernetes, IAM from real production use | Multi-cloud breadth |
| K8s at scale | Operated production clusters with a service mesh and multi-region deployments | Karpenter, advanced mesh tuning (sidecarless) |
| Edge networking | CDN/WAF architecture: rate limiting, DDoS/abuse protection, routing | Anycast and global LB design at scale |
| Infrastructure-as-code | Terraform/Pulumi with a reproducibility-first mindset | Progressive/safe rollout of infra changes, drift control |
| SWE fundamentals | Write real production code (Go/Rust/TS/Python), not just config | Strong CS fundamentals under a no-AI time box |
| Cost engineering | Awareness of cost-vs-reliability tradeoffs | Attribution/chargeback and waste identification at scale |
| Migration | - | Infrastructure migration and platform-unification experience |
The required column is table stakes. The nice-to-haves are where you differentiate - and where the cost and migration pillars live.
"Strong SWE fundamentals" is not filler on an infra JD. The no-AI phone screen is a real coding round, so you must write clean production code under a time box without AI assistance - config fluency alone won't clear it. Many infra candidates underweight this and lose the loop on the screen.
Build an honest gap mapdo this before any other prep
Sort every pillar into one of three buckets. The bucket decides how you spend prep time and how you talk about each area in the loop.
- Lived
- You've run it in production and have a war story. Lead with these; this is your deep-dive material.
- Adjacent
- You've touched it or own a neighboring system. Bridge from what you know; be honest about the edge of your experience.
- Gap
- You'd be studying it. Study the fundamentals now and, in the loop, reason from first principles rather than bluffing depth.
Interactive diagram. Tab through its regions; each focused region shows its detail in the panel below.
Same gap, two responses - in a truth-seeking loop the gap isn't the failure, the bluff is.
Truth-seeking is an explicit value in this loop, so bluffing a gap is double-negative: you risk being caught and you fail the value test even if you aren't. "I haven't run a service mesh in production, but here's how I'd reason about the sidecar tradeoff" beats a confident wrong answer every time.
- 1List the pillars. Network, orchestration, edge/security, cost, IaCInfrastructure as Code. Managing servers and cloud resources through version-controlled config files (e.g. Terraform)., SWE fundamentals.
- 2Bucket each one. Lived, adjacent or gap - be ruthless and specific.
- 3Attach a story to every Lived pillar. A concrete incident, decision or migration you owned.
- 4Pick two Gaps to close first. Usually the no-AI coding screen and whichever pillar shows up most in the system-design round.
Takeaway. Qualified means a senior IC who ships from week one; build an honest gap map (lived / adjacent / gap), lead with lived stories and never bluff - in a truth-seeking loop, "I haven't done X, but here's how I'd reason about it" wins.
Self-check
QYou have deep AWS networking and EKS experience but have never operated a service mesh in production. How should you handle that gap in the loop?
Team shape and how you'll work
After this you can set expectations for the flat, talent-dense, ship-fast culture.
Cursor's engineering org is deliberately small, flat and talent-dense and famously lean relative to the scale it serves. That shape is the reason every hire is expected to ship from week one.
A small team supporting 1M+ DAU has a clear implication for you: there's no layer of people between you and the consequence of your work. You own ambiguous problems end-to-end and the absence of process is a feature you're expected to thrive in, not a gap you complain about.
- Org shape
- Flat, small, talent-dense; lean headcount relative to the user base it supports.
- Locations
- San Francisco and New York; expect in-person, high-bandwidth collaboration.
- How work starts
- Bottom-up experimentation; independent teams spin up to chase exploratory ideas.
- What you own
- Ambiguous problems end-to-end - you scope, build and operate, not just execute a ticket.
What flat-and-lean asks of youthe behaviors it rewards
- Bias to ship: a working, scoped-down system this week beats a perfect design next quarter.
- End-to-end ownership: you take a vague problem, define the boundaries yourself and operate what you build.
- Cost/reliability judgment: you make explicit tradeoffs instead of gold-plating, because there's no one downstream to clean up over-engineering.
- Comfort with pace and ambiguity: you treat missing process as room to move and you bring the structure yourself.
- Spirited, evidence-based debate: you argue hard for a position and change your mind when the data says so.
The 2-day on-site - building real work with the team, sharing meals, presenting at the end - exists to filter people "just viewing it as a job." For infra, it's also a working sample of the flat culture: you'll be expected to scope, build and defend a real decision in two days, the way you would in week one.
What this means for your prepturn culture into preparation
- 1Prepare ambiguous-problem stories. A time you scoped and shipped infra with no spec - that's the lived proof of end-to-end ownership.
- 2Rehearse a defensible tradeoff out loud. Pick a cost-vs-reliability call you made and be ready to defend it under pushback.
- 3Practice changing your mind. Have one example where evidence flipped your position; truth-seeking is graded.
- 4Actually use Cursor, deeply. Genuine passion for the product over job-shopping is an explicit filter and infra owns the foundation under the thing you'd be using daily.
I do my best work where the team is small enough that I own a problem end-to-end. Give me an ambiguous cost or latency problem under the product, let me scope it down to something I can ship this week and let me defend the tradeoff - that's the environment I'm looking for and it's why a flat infra team at this scale appeals to me.
Don't signal that you need a spec, a manager's sign-off or a quarter of ramp before you contribute. On a flat team that ships week one, "I'd spend my first three months learning before touching anything" reads as a mismatch. Show you can ship a contained, correct thing early while you learn the rest.
Takeaway. Cursor's infra team is flat, lean and talent-dense, so you own ambiguous problems end-to-end and ship from week one - prepare stories of scoped shipping, a defensible tradeoff and a mind genuinely changed by evidence.