Interview Prep: TypeScript Challenges Inspired by Real‑World Problems (Maps, LLMs, Embedded)

Hook: Interview prep that mirrors real world pain

Senior TypeScript engineers rarely get asked toy problems in interviews anymore. Hiring teams want evidence you can ship robust, typed systems that handle messy realities: offline maps, streaming LLM responses, and telemetry for constrained embedded devices. This guide gives you a set of practical interview questions and take-home challenges modeled on those real problems, plus scoring rubrics and sample TypeScript patterns you can use in interviews and take-home submissions.

Why these modules matter in 2026

By early 2026 the industry is converging on three realities that change how TS engineers design systems:

• LLM integrations everywhere. Vector tiles, client-side route calculations, and privacy-conscious offline maps are common in consumer and enterprise apps.
• LLM integrations everywhere. From server-side embeddings to on-device agents like the 2025 research previews and desktop agents, production systems must stream, validate, cache, and rate-limit LLM outputs safely.
• Embedded + telemetry governance. Safety-critical sectors (automotive, industrial) demand typed telemetry schemas, deterministic timing analysis, and schema versioning tied to verification workflows.

These trends change the evaluation criteria for senior candidates: typing rigor, schema migrations, backpressure strategies, and explainable LLM safety hooks are now first-class concerns.

How to use this guide

Use each module for one of three interview formats:

• Screening: 5 quick questions (15 minutes) to check conceptual fit.
• Live interview: 45-60 minutes for architecture and whiteboard design questions.
• Take-home: 4-8 hours deliverable with tests and a typed API surface.

Every take-home includes: spec, acceptance criteria, test harness outline, and a scoring rubric. Emphasize TypeScript typings, tests, docs, and a short design write-up.

Module A: Typed Mapping Features

Context and common hiring goals

Companies need engineers who can build performant, maintainable map features: typed GeoJSON models, client-side spatial indexes, route caching, and offline-first sync. Interviewers look for strong TS patterns, spatial algorithm understanding, and pragmatic tradeoffs.

Screening questions

1. How would you represent a point, line, and polygon in TS so that downstream APIs and renderers can rely on types?
2. What is a geohash and when would you use it vs an R-tree?
3. Explain vector tiles vs raster tiles and a use case for each.
4. How would you model precision and coordinate reference systems in your types?
5. Describe a cache invalidation strategy for offline map tiles.

Live system design prompt

Design a client-side component that supports:

• Displaying POIs from multiple sources (Mapbox vector tiles, on-device dataset, remote API)
• Local search within a map viewport
• Offline caching and eviction

Discuss data model, indexing, synchronization, and how TypeScript types will prevent common runtime errors.

Take-home challenge: Typed POI Service

Time: 6 hours suggested. Deliverables: repo, tests, README, short design note.

Spec:

• Implement a small library that merges POIs from three sources into a typed stream API.
• Sources: localVectorTiles, remoteAPI, deviceCache. Each source returns a different shape.
• Expose a single async iterator or Observable that yields deduplicated, typed POIs in viewport order.
• Provide types that guarantee the consumer receives canonical POI objects with stable ids and a provenance field.
• Support an in-memory R-tree for spatial queries and pagination.

Type requirements

type RawVectorTileFeature = { t: 'vt'; id: string; geom: number[]; props: Record<string, any> }

type RemotePOI = { source: 'remote'; uid: string; lat: number; lon: number; name?: string }

type CachedPOI = { source: 'cache'; id: number; location: [number, number]; meta: object }

// Canonical POI
interface POI {
  id: string // stable, derived from provenance
  name?: string
  location: { lat: number; lon: number }
  provenance: 'vt' | 'remote' | 'cache' | 'merged'
}

Acceptance criteria

• All tests pass for merging and dedupe
• Types enforce canonical POI shape at compile time
• Async iterator yields items in viewport order (nearest-first)
• Documentation shows how to extend a new source

Sample evaluation rubric

• Correctness and test coverage: 40%
• Type quality and ergonomics: 25% (use of discriminated unions, mapped types, branded types)
• Performance and algorithmic choices: 20%
• Docs and extension points: 15%

Example TypeScript pattern

// branded id to avoid accidental mixing
type Brand<T, B> = T & { __brand?: B }

type POIId = Brand<string, 'POIId'>

function makeId(src: string, id: string): POIId {
  return (src + ':' + id) as POIId
}

Module B: LLM Integrations

Why LLM-focused interviews changed

In 2026 teams integrate LLMs as first-class services and sometimes on-device agents. Interviewers assess typed prompt pipelines, streaming handling, safety filtering, caching, and cost controls. References: recent research previews of desktop agents and the emergence of tool-using agents in production.

Screening questions

1. How would you type a streaming LLM response in TypeScript and consume it safely?
2. What are typed prompt templates and why do they matter?
3. Describe a type-safe wrapper around an embeddings API.
4. How do you version and migrate prompt templates and response schemas?
5. Explain safety filters at the type level vs runtime checks.

Live coding / system design prompt

Design a middleware that sits between your app and an LLM provider. It should:

• Enforce typed request/response contracts
• Support streaming tokens and cancelation
• Rate-limit per user and cache embeddings
• Emit telemetry events with typed schemas (telemetry patterns)

Take-home challenge: Type-safe LLM bridge

Time: 8 hours suggested. Deliverables: repo, tests, README, infra notes.

Spec:

• Implement a thin typed wrapper over a mock LLM API that returns token streams.
• Expose a PromptTemplate type that validates placeholders at compile-time where possible.
• Implement a typed ResponseSchema for a structured output (example: invoice fields) and a validator pipeline using a runtime schema library (zod, io-ts, or custom).
• Provide cancellation and backpressure support via AbortSignal and async iterators.
• Implement simple in-memory embedding cache with a typed key and TTL.

Type patterns

type PromptVars = Record<string, string | number>

interface PromptTemplate<TVars extends PromptVars> {
  template: string // e.g. 'Summarize: {{text}}'
  requiredVars: (keyof TVars)[]
  fill(vars: TVars): string
}

// Structured response
const InvoiceSchema = z.object({
  total: z.number(),
  items: z.array(z.object({ desc: z.string(), price: z.number() }))
})

type Invoice = z.infer<typeof InvoiceSchema>

Acceptance criteria and tests

• All typed compile-time checks pass
• Runtime validation rejects malformed LLM outputs and logs telemetry
• Streaming tokens can be canceled gracefully
• Embedding cache returns typed values and respects TTL

Evaluation rubric

• Type-safety and ergonomics: 30%
• Robustness of streaming and cancelation: 25%
• Validation and safety handling: 25%
• Test coverage and docs: 20%

Notes on trends and risks

Expect questions about privacy, on-device LLMs, and autonomous agents. In interviews, show that you can keep typed boundaries between user data, prompt composition, and downstream tools. Cite mitigation: prompt sanitation, typed safety layers, and rate limits.

Module C: Embedded Systems and Telemetry Pipelines

Context

Telemetry for embedded devices must be compact, typed, and verifiable. The Vector acquisition news and investments in timing analysis underline the industry's focus on worst-case execution time and traceable telemetry. Candidates must show familiarity with typed schemas, protobufs, binary formats, and migration strategies.

Screening questions

1. Why choose Protobuf or FlatBuffers for telemetry vs JSON?
2. How do you design a versioned telemetry schema with forwards/backwards compatibility?
3. Explain sampling, batching, and backpressure strategies for low-bandwidth devices.
4. How do you test timing-related behavior in TypeScript code that will run on devices?
5. What tools exist for telemetry observability and typed ingestion in Node/edge?

Live system design prompt

Design a telemetry ingestion pipeline that accepts typed events from embedded devices, validates schema, performs dedupe, and writes to long-term storage. Include error handling for schema drift and a migration plan.

Take-home challenge: Typed Telemetry SDK

Time: 6 hours suggested. Deliverables: repo, tests, README, migration note.

Spec:

• Build a small SDK that emits typed telemetry events to a mock collector over HTTP or UDP.
• Provide two transport modes: reliable (HTTP) and best-effort (UDP-like). Types must reflect reliability guarantees.
• Implement event envelope typing with version fields and a serializer that can switch between JSON and compact binary (simulate binary).
• Add a migration test that demonstrates adding a new optional field while maintaining compatibility.

Type example

type TelemetryV1 = {
  v: 1
  deviceId: string
  ts: number
  metrics: { cpu: number; mem: number }
}

type TelemetryV2 = TelemetryV1 & { v: 2; metrics: { cpu: number; mem: number; temp?: number } }

type Envelope<T> = { env: string; payload: T }

Acceptance criteria

• Typed serializers for both JSON and compact formats
• Tests demonstrating forward/backwards compatibility
• Transport modes behave as typed contracts indicate
• Design note referencing WCET and verification considerations

Evaluation rubric

• Schema correctness and migration story: 35%
• Typed transport abstraction and ergonomics: 25%
• Test coverage and simulation of reliability: 20%
• Security and privacy considerations: 20%

Cross-cutting TypeScript techniques to evaluate

• Discriminated unions for heterogeneous event and source modeling.
• Branded types to prevent mixing identifiers.
• Conditional and mapped types to derive canonical shapes from raw inputs.
• Type-level checks for template placeholders using template literal types.
• Runtime validators (zod/io-ts) integrated with TS types for safe boundaries.

Sample interview questions for senior candidates

1. Design a typed plugin interface for map renderers that allows different tile formats while preserving compile-time guarantees.
2. How would you instrument an LLM pipeline so every prompt and response has a typed audit trail for compliance?
3. Given a telemetry event schema, show a migration plan and write a small migration tool in TS that upgrades v1 to v2 events safely.
4. Discuss tradeoffs between doing heavy geospatial work client-side vs server-side. How would types influence your decision?
5. Describe how you would test timing characteristics of an embedded TypeScript runtime (for example, QuickJS or Deno on device) and tie results to WCET estimates.

Practical interview tips and grading shortcuts

• Require a one-page design note for every take-home. It reveals reasoning faster than code alone.
• Use a smoke test harness that runs key flows; prioritize passing tests over perfect implementations.
• Score types explicitly: ask candidates to explain the most complex type and why they chose it.
• For live interviews, whiteboard a data flow and ask for key types only; deep dive into one tricky area.

Actionable takeaways for candidates

• Practice writing discriminated unions and branded IDs for systems that merge heterogeneous inputs.
• Get comfortable with runtime validation libraries and show how they map to TS types in your code.
• Build a small streaming demo using async iterators and AbortSignal to showcase cancelation and backpressure handling.
• Prepare migration notes for any schema changes; show tests that prove forwards and backwards compatibility.

By focusing on typed boundaries and pragmatic runtime checks, you reduce risk and make systems easier to verify — a must-have skill in 2026.

Future-facing notes: what interviewers will ask in 2026

Expect questions that connect TypeScript with system-level constraints: on-device LLM rate limits, telemetry bound to WCET proofs, and offline map consistency models. Candidates who can demonstrate type-driven design plus a clear migration and observability plan stand out.

Final checklist for hiring teams and candidates

• Provide clear time boxes and acceptance criteria for take-homes.
• Score type quality explicitly during review.
• Test for operational thinking: how does this code behave in failure modes?
• Ask candidates to explain tradeoffs and list one improvement they'd prioritize in production.

Call to action

If you found this guide useful, clone our ready-made interview repo at the typescript.page interview kit repository, try the take-home templates, and adapt the rubrics for your hiring process. Want a customized set of challenges tailored to your stack? Contact our team to workshop role-specific prompts and scoring sheets.

Related Reading

• Automating Legal & Compliance Checks for LLM‑Produced Code in CI Pipelines
• Case Study: Simulating an Autonomous Agent Compromise — Lessons and Response Runbook
• Edge AI Reliability: Designing Redundancy and Backups for Raspberry Pi-based Inference Nodes
• Developer Review: Oracles.Cloud CLI vs Competitors — UX, Telemetry, and Workflow
• A Faithful Guide to Translating Pop Culture References in Tafsir for Young Audiences
• Tested: Which Disposable Heating Packs Actually Keep Delivery Pizza Hot?
• Syllabus Supplement: Contemporary China Through Memoirs and Essays
• Sourcing Low-Cost Electronics: Red Flags and Quality Tests When Buying from Marketplaces
• How to Pitch a Beauty Line to a Transmedia Studio: Lessons from The Orangery