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LinkedIn ML Platform Engineer interview questions

LinkedIn runs a large internal AI platform for ranking, recommendation and generative features and hires infrastructure engineers for GPU training and serving at that scale, feature and embedding systems, and the platform's reliability, in the US and in its Bengaluru engineering centre. The preparation that fits is ML platform design for high-volume ranking and retrieval workloads, GPU capacity and scheduling across internal teams, and serving with strict latency budgets; the general loop is LinkedIn's backend engineering loop. We have not found an AI-infrastructure-specific first-hand debrief and do not list unconfirmed rounds.

ML PLATFORMS AT PRODUCT COMPANIES

The model serves a product that would exist without it, so the interview weights platform, data and reliability over raw GPU depth.

Loop leans on: ML platform, data infrastructure, serving reliability, developer experience. Compare the other ml platforms at product companies →

16 questions to practise16 concepts to master4 core topicsrole: ML Platform Engineer

The LinkedIn ML Platform Engineer interview process

Limited public data
RoleAI platform infrastructure engineer
No reliable public breakdown of the loop; the requirements above come from postings. Rounds unconfirmed. Specific postings were not fetchable in the September 2026 pass.
WHAT THEY'RE EVALUATING
  • GPU training and serving for ranking and generative features
  • Feature and embedding systems; platform reliability

Compiled from our research and publicly available information (candidate reports and company interview guides). Interview loops change and are continuously iterated, and they vary by team, level, and region. Treat this as directional preparation, not an official spec, and confirm the exact rounds with your recruiter or hiring point of contact.

LinkedIn ML Platform Engineer salary

What we can trace, labelled by where it came from. We publish a band only where there is a source behind it, so some of this page is a gap rather than a number.

NO TRACEABLE BAND

We have not found a compensation figure for this role at LinkedIn that we can trace to an employer posting or a public aggregator. Rather than publish an estimate, we are naming the gap. Their careers page is the authority, and postings in some jurisdictions are required to state a range.

HIRING FROM INDIA
Multinational with an India engineering centre

An established India presence, usually Bengaluru, Hyderabad or Pune, hiring on a local band with the parent company's level structure. Far more attainable than the global-remote route, with listed-company equity and the usual multinational benefits.

LEVELREPORTED FOR THIS EMPLOYER TYPE
Early career (IC1-IC2 equivalent)₹26 LPA - ₹45 LPA
Senior (IC3 equivalent)₹37 LPA - ₹85 LPA
Staff and above (IC4+ equivalent)₹69 LPA - ₹1.4 Cr

Reported total compensation for NVIDIA software engineers in India by level, per levels.fyi self-reports (accessed September 2026; IC3 median about ₹62 LPA, IC4 median about ₹94 LPA), used as the reference for this employer type. Not a figure reported for this company or for this exact title; bands vary by internal level and by company.

Full method, US bands by level, and the three India tiers side by side are in the AI infra salary guide, including what actually moves your number between these tiers.

Representative ML Platform Engineer questions for LinkedIn's loop

LinkedIn's loop draws from these tracks. Here are the highest-signal questions in each, ordered by what candidates rate most useful.

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Go deeper on the topics LinkedIn's loop tests

The tracks that map to a LinkedIn ML Platform Engineer loop, ordered easy to hard.

AI Infrastructure System Design31 →LLM Inference & Serving30 →Kubernetes, Slurm & GPU Scheduling30 →Napkin Math, Cost & Capacity30 →

The concepts LinkedIn's ML Platform Engineer loop assumes you know

The vocabulary and mental models behind LinkedIn's questions, from our curriculum. Start with the foundations free; the deeper, interview-defining ideas are part of premium.

AI SYSTEMS DESIGN

Foundational
Inference Platform ArchitectureAn LLM inference platform is the layer between a product's API call and a GPU running a serving engine, and every design round starts from its reference shape: a gateway that authenticates and rate-limits, a router that picks a replica with the right model and a warm cache, a per-replica scheduler that batches, engines that run prefill and decode, a KV cache tier, an autoscaler, and the observability that makes it operable. This page draws that shape, sizes each box for a concrete workload, and walks the derivation from user demand to replica count that every design answer has to contain.
Advanced🔒 Premium
Request Routing and Load Balancing for LLMsA load balancer for stateless web services spreads requests evenly and is done. A router for LLM replicas has two things a web balancer never had to think about: each replica holds a cache (the KV pages of recent prefixes) that makes some replicas far cheaper than others for a given request, and each request costs a wildly different amount, so counting connections is meaningless. This page builds the router that handles both: prefix-aware placement with load-aware fallback, cost-aware queue estimates, session affinity, and the failure handling when a replica restarts and its cache is gone.
CoreSign in
GPU Job Scheduler DesignDesign a scheduler for a shared GPU cluster is the most common design prompt in AI infrastructure interviews, because it touches everything: queues and priorities, gang placement, topology, fairness across teams, preemption and the checkpoints that make it survivable, and the failure handling that keeps a 512-GPU job alive. This page builds the design in layers, states the data model and the scheduling loop, derives the numbers (how long a job waits, how much preemption costs, how much fragmentation wastes), and lists the trade-offs the interviewer will push on.
Advanced🔒 Premium
Training Cluster Design at 10k GPUsDesign a cluster for training frontier models is the prompt that tests whether a candidate can hold hardware, network, storage, scheduling and reliability in one head at once. The answer is a bill of materials with a reason for every line: how many GPUs and why, how they are grouped into pods, how the fabric connects the pods and what it costs a collective to cross one, how much storage bandwidth the checkpoints and the data loader need, how power and cooling bound the whole thing, and how the failure statistics set the spare pool and the checkpoint cadence. This page derives each line for a 10,240-GPU cluster.

INFERENCE & SERVING

Foundational
Prefill vs DecodeAn LLM request runs in two phases with opposite hardware profiles: prefill reads the whole prompt in one compute-bound pass and decides time to first token, decode emits one token per forward pass and is bound by memory bandwidth. Every serving decision, from batch size to which GPU to buy to whether to split the two phases across machines, follows from that split.
Foundational
The KV CacheThe KV cache stores each token's attention keys and values so decode never recomputes them, turning a quadratic cost into a linear one at the price of memory that grows with every token in every concurrent sequence. Its size, 128 KB per token for Llama 3.1 8B and 320 KB for 70B in bf16, is what caps concurrency and context on a given GPU, so it decides batch size, replica count and whether a model fits at all.
CoreSign in
Continuous BatchingContinuous batching schedules at the granularity of a single decode step instead of a whole request, so a finished sequence's slot is refilled on the next iteration rather than when the longest request in the batch ends. It is the scheduling idea that turned LLM serving from a padded, half-idle GPU into one that stays full, and it decides how the engine's scheduler, memory manager and latency SLOs interact.
Advanced🔒 Premium
PagedAttentionPagedAttention stores the KV cache in fixed-size blocks scattered across HBM and maps each sequence's logical positions to physical blocks through a block table, the same trick an operating system uses for virtual memory. It removes the reservation and fragmentation waste of contiguous allocation, lets blocks be shared between sequences, and is why an engine can decide admission by counting free blocks.

SCHEDULING & ORCHESTRATION

Foundational
Kubernetes GPU SchedulingKubernetes knows nothing about GPUs until something tells it. The NVIDIA device plugin advertises each node's GPUs as a countable resource, the scheduler matches a pod's request to a node with enough of them, and the container runtime wires the device in. That model is enough for one job per GPU and breaks the moment you need sharing, topology or multi-node placement, which is where Dynamic Resource Allocation, the GPU Operator and the batch schedulers come in. Knowing which layer does what is the platform interview's opening question.
CoreSign in
MIG, MPS and Time-SlicingA whole H100 is far more than a notebook, a small inference service or a CI job needs, and giving each of them a card leaves most of the fleet idle. Three mechanisms share a GPU, and they differ in what they isolate: MIG partitions the hardware into up to seven slices with their own memory and compute, MPS lets several processes share one GPU's SMs concurrently with no memory isolation, and time-slicing context-switches between processes with no isolation at all. The choice is the isolation the workload needs against the utilization the platform wants.
Advanced🔒 Premium
Gang Scheduling with Kueue and VolcanoA distributed training job is 64 pods that start together or not at all: if 40 are running and 24 are Pending, the 40 hold their GPUs idle at a collective barrier waiting for ranks that may never come, and two such jobs can deadlock a whole cluster. Gang scheduling makes the job the unit of admission. Kueue and Volcano add queues, quotas, priorities and preemption on top, which is what turns a pile of GPUs into a platform several teams can share without starving each other.
Advanced🔒 Premium
Topology-Aware SchedulingTwo placements of the same 64-GPU job can differ by 2x in step time: one keeps every tensor-parallel group on a single NVSwitch node and every data-parallel ring on a single rail, the other scatters ranks across racks and pushes per-layer traffic through the spine. The scheduler is the only thing that can prevent the second placement, because the framework maps ranks to whatever GPUs it is handed. Topology-aware scheduling means the scheduler knows the hierarchy (NVLink domain, rail, rack, spine block) and places gangs to keep traffic low in it.

NAPKIN MATH & CAPACITY

Foundational
Model Memory FootprintThe first calculation in almost every AI infra loop: how many bytes does this model occupy, for inference and for training, and does it fit on the card in front of you? Inference is parameters times bytes per parameter (2 in bf16), plus a KV cache that grows with users. Training is 16 bytes per parameter before activations. A 70B model is 141 GB to serve and 1.13 TB to train, and a reader who can produce those two numbers from the parameter count, with the reasoning, has passed the first five minutes of the estimation round.
Foundational
KV Cache SizingThe KV cache is the memory that decides how many users a serving replica can hold and how long their context can be. Its size per token comes from four numbers in the model's config file (layers, KV heads, head dimension, bytes per element) and one formula; multiplied by context and concurrency it is the number every capacity plan is built on. This page derives it, works it for four models including an MLA one, and shows the two places candidates get it wrong by a factor of eight.
Foundational
Training FLOPs: 6NDThe compute needed to train a language model is six floating-point operations per parameter per token: two for the forward pass and four for the backward. Multiply by the parameter count and the token count and you have the whole run's compute, which is the number every fleet-sizing, time-to-train and cost question starts from. This page derives the 6, states the attention correction and when it matters, and shows where the 2N of inference comes from, so the reader can rebuild the formula rather than recall it.
Advanced🔒 Premium
Bandwidth-Bound Decode ThroughputBecause decode reads every weight once per step, its speed is a division: memory bandwidth over bytes per step. That one formula gives single-stream tokens per second for any model on any card, the batch curve that flattens at the ridge point, the effect of quantization, and the point where the KV cache rather than the weights becomes the thing being read. This page derives it, works it for a 70B model on four accelerators, and shows how to read a vendor throughput claim against it.
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Where to apply, and official LinkedIn resources

Straight from LinkedIn: open roles and the company's own hiring guidance. Prep here, then apply there.

ApplyCareers at LinkedIn

External links to LinkedIn's own pages. Roles and processes change; always confirm on the official site.

ABOUT THE ROLE
What is an AI infrastructure engineer?What AI infra roles payWho else is hiringThe skills AI infra loops test
LINKEDIN INTERVIEW FAQ
Does LinkedIn hire AI infrastructure engineers?▲

Yes, for its internal AI platform (GPU training and serving, feature and embedding systems, platform reliability) in the US and in Bengaluru; specific postings were not fetchable in our September 2026 pass, so check the careers site for current titles.

What does the LinkedIn AI infrastructure interview test?▼
What is the LinkedIn AI infrastructure engineer salary?▼

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