Full Stack Engineer, Live & Game

Level: Middle-level Full Stack Engineer with at least 3 years of experience building consumer-facing products across frontend and backend systems.

Required skills: Proficiency in one backend language such as Node.js, Go, Java, or Rust, one frontend framework, AI coding tools, strong software engineering fundamentals, and an understanding of performance, reliability, and latency.

Relevant experience: The ideal candidate should be familiar with owning features end to end, deploying and supporting production systems, and building real-time experiences such as live streaming, messaging, leaderboards, gaming services, or WebSocket-based interactions under unpredictable, high-volume traffic.

How would you design a real-time architecture for messaging, leaderboards, and in-game interactions during a sudden traffic spike?

I would use horizontally scalable stateless services, WebSockets or a managed pub/sub layer for delivery, and partitioned state stores for leaderboards and game data. I would also add rate limiting, backpressure, connection draining, and autoscaling based on active connections and event throughput.

What are the main differences between HLS and RTMP, and how would you use them in a live streaming product?

RTMP is commonly used for ingesting live video from an encoder, while HLS is typically used to distribute video to viewers through HTTP-based delivery and CDNs. I would use RTMP or a similar protocol for ingest, then transcode and package the stream into HLS, potentially using low-latency HLS when interactive latency is important.

How would you reduce video playback latency while maintaining reliability across different devices and network conditions?

I would use adaptive bitrate streaming, short segments, low-latency HLS where supported, and CDN edge delivery. The client should monitor buffer health and bandwidth, switch renditions smoothly, recover from stalled segments, and fall back to more compatible playback modes when necessary.

How would you scale WebSocket connections during a synchronized audience surge?

I would distribute connections across load-balanced WebSocket gateways and avoid storing connection state only in one process. Shared presence and pub/sub infrastructure would allow messages to reach users regardless of gateway placement, while connection limits, heartbeats, admission control, and graceful degradation would protect the system during overload.

How would you implement a highly scalable leaderboard with frequent score updates?

I would partition leaderboards by game, region, or event and use an in-memory sorted data structure such as Redis sorted sets for fast ranking operations. Durable persistence could be handled asynchronously through an event stream, with idempotent consumers and periodic snapshots to recover from failures.

How would you ensure game state remains correct when clients experience latency, retries, or duplicate requests?

The server should be authoritative and validate every state-changing action rather than trusting the client. Requests should include unique operation IDs or sequence numbers, and state transitions should be atomic and idempotent so retries cannot apply the same action multiple times.

What strategies would you use to protect backend services and databases from correlated write spikes?

I would buffer noncritical writes through a durable queue or event stream, batch updates where possible, and separate transactional data from derived analytics. I would also use caching, database partitioning, connection pooling, rate limits, circuit breakers, and load tests that model synchronized traffic rather than only steady-state volume.

How would you design a frontend that remains responsive during real-time updates and high event volume?

I would keep rendering work bounded by batching updates, throttling nonessential UI changes, virtualizing large lists, and updating only affected components. The client should also handle reconnects, stale events, offline states, and graceful degradation when real-time updates become too expensive.

How would you test a live feature before deploying it to millions of users?

I would combine unit and integration tests with contract tests, browser tests, protocol-level tests, and load tests that simulate connection storms and bursty event fan-out. I would deploy progressively with feature flags, synthetic monitoring, dashboards, automated rollback criteria, and production-like failure testing.

How do you use AI coding tools while maintaining code quality and production accountability?

I use AI to accelerate exploration, implementation, test generation, and debugging, but I remain responsible for reviewing its output and validating assumptions. I would require strong typing, focused tests, security checks, observability, code review, and staged deployment before considering an AI-assisted feature production-ready.

Beast Industries, the media and entertainment company founded by MrBeast, is rebuilding live streaming and gaming experiences for a global audience of more than 100 million people. As a Full Stack Engineer, you’ll build real-time video, messaging, leaderboards, and in-game features end to end, using AI coding tools across the frontend and backend. The stack includes Node.js, Go, Java, Rust, modern frontend frameworks, WebSockets, messaging systems, and live streaming technologies such as HLS and RTMP. This is a Bay Area-based role offering $178,000–$223,000 base salary plus equity and benefits—an exciting opportunity to shape zero-to-one products at extraordinary scale with a fast-moving, high-impact team.