Are these based on real engineering roles?
Yes. The catalog is built around real engineering role patterns so the practice round feels closer to a live interview.
Role practice
Infrastructure interview practice
Pick a role, answer follow-up questions out loud, and get a scored verdict after the interview.
11 rolesKubernetesDistributed SystemsGoRust
Create custom interviewRoles to practice
Anthropic
Infrastructure
Staff+ Software Engineer, Infrastructure
Staff Infrastructure Engineers build and scale the clusters that train Claude and the production systems that serve it reliably to millions of users, solving novel scaling challenges few organizations face. They lead design of large training and serving infrastructure, balancing reliability, cost, and developer velocity. A technical interview would probe large-scale distributed systems design (fault tolerance, scheduling, storage), reasoning about failure modes in a multi-thousand-node GPU cluster, and tradeoffs in building platforms that let researchers iterate quickly without sacrificing production stability.
PythonKubernetesDistributed SystemsCerebras
Infrastructure
Compiler Engineer, Advanced Technology Group
Designs and develops the Tungsten language compiler that maps deep-learning workloads onto the Cerebras Wafer-Scale Engine, owning IR design, optimization passes, and code generation for a massively parallel custom-silicon target. Collaborates with hardware and kernel teams to extract performance from the WSE's on-wafer memory and fabric. A technical interview would probe compiler internals (dataflow analysis, loop/tiling transformations, IR lowering through MLIR/LLVM) and reasoning about scheduling computation across a non-von-Neumann architecture.
C++MLIRLLVMCognition
Infrastructure
Research Engineer, Infrastructure
Build the training and evaluation infrastructure behind Cognition's models for Devin, owning the data, compute, and harness that let researchers iterate quickly on post-training and agent capabilities. You'll bridge ML research and large-scale systems engineering. A technical interview would probe distributed training pipelines, large-scale data and eval harness design, and how you'd build infrastructure that makes model and agent experiments fast, reproducible, and cheap to run.
PythonPyTorchCUDACursor
Infrastructure
Software Engineer, Infrastructure
Build and scale the backend systems that serve Cursor's AI code editor to over a million daily active users, including the indexing, retrieval, and request-serving paths that keep latency low under heavy load. You'll own high-throughput services where a few milliseconds of overhead is multiplied across millions of completions. A technical interview would probe distributed systems design, low-latency service architecture, and how you'd shard, cache, and scale a code-indexing pipeline under real production load.
RustTypeScriptDistributed SystemsPalantir
Infrastructure
Backend Software Engineer - Infrastructure
Build the lowest layers of the stack underpinning Palantir Foundry and Gotham, working on distributed systems, database technologies, and large-scale data processing using Java and Go alongside open-source systems like Cassandra, Spark, and Elasticsearch. You'd ship performant, secure, scalable building blocks that the entire product platform depends on, deployed to demanding public- and private-sector institutions. A technical interview would probe data structures and algorithms, distributed-systems fundamentals such as consistency, partitioning, and fault tolerance, and your ability to design and debug a high-throughput storage or compute system.
JavaCassandraSparkPerplexity
Infrastructure
AI Infrastructure Engineer (Member of Technical Staff)
Build and operate the large-scale compute and orchestration platform that powers Perplexity's training and serving workloads, working with Kubernetes, Slurm, PyTorch, and primarily AWS. You'll manage GPU clusters, job scheduling, and the reliability of infrastructure shared across research and product teams. A technical interview would probe distributed training infrastructure, container and cluster orchestration at scale, and how you'd debug a multi-node GPU job that's bottlenecked on networking or scheduling.
KubernetesPyTorchSlurmRamp
Infrastructure
Senior Software Engineer, Infrastructure
Builds and operates the cloud platform underpinning Ramp's products, owning compute, deployment, observability, and reliability across AWS so product teams can ship safely at fintech scale. Improves CI/CD, infrastructure-as-code, and the resilience of services handling real-time money movement. A technical interview would probe cloud architecture and reliability (scaling, failure domains, observability), infrastructure-as-code design, and how you would diagnose and remediate a production reliability incident.
AWSKubernetesTerraformReplit
Infrastructure
Software Engineer, Compute Platform
Build and scale the distributed compute infrastructure that runs millions of user workspaces and powers apps created by Replit Agent, optimizing performance and efficiency across global regions. The role covers container orchestration, resource isolation, and launching new cloud primitives that the Agent uses to deploy complex applications. A technical interview would explore distributed-systems fundamentals, container/VM isolation and scheduling, and how to drive down cold-start latency and cost while keeping multi-tenant workloads secure.
GoRustKubernetesStripe
Infrastructure
Backend Engineer, Core Technology
Builds the large-scale distributed infrastructure that internal and external Stripe teams depend on, spanning compute, networking, distributed caching, document storage, and data-serving systems like Trino/Presto, Apache Pinot, and ElasticSearch. Owns roadmap planning, production reliability, and debugging issues across services and the stack. A technical interview would probe distributed-systems design (consistency, partitioning, caching), API and service modeling, and debugging a production incident across a multi-service request path.
GoJavaTrinoxAI
Infrastructure
Network Engineer, ML Infrastructure (High-Speed Interconnects)
This role designs, validates, and productizes the high-speed copper and optical interconnects (PAM4 SerDes, silicon photonics, optical transceivers) that wire together xAI's massive GPU training clusters, and collaborates with training teams to optimize network topology for AI workloads. It also drives failure analysis of interconnect hardware in production superclusters. A technical interview would probe high-speed interconnect and SerDes fundamentals, GPU cluster network topology (fat-tree/rail-optimized designs, RDMA/RoCE), and diagnosing performance degradation or link failures that bottleneck collective communication at scale.
InfiniBandRDMANetwork TopologyxAI
Infrastructure
Software Engineer, Infrastructure
Infrastructure engineers design and maintain the distributed systems powering xAI's Colossus supercluster, working across Kubernetes scaling (controllers, admission plugins), Envoy-based load balancing, observability, and exabyte-scale storage. The goal is efficiency, reliability, and performance for compute and data platforms at extreme scale. A technical interview would probe large-scale distributed systems design, deep Kubernetes internals and custom controllers, traffic-shaping and load-balancing with Envoy, and reasoning about throughput and failure handling in high-QPS production systems.
GoRustKubernetes
FAQ
Is the interview voice-based?
Yes. ExoForm runs a live voice interview, asks follow-ups, and produces structured feedback after the session.
Can I try it for free?
Yes. You can start with the free interview allowance before upgrading for more practice.