← roblox / Senior Product Manager, Compute Platform
brief / art_OMTPi2gz-_A
role
model
anthropic/claude-sonnet-4.6
created
2026-06-15T19:24
Company snapshot
Roblox is a user-generated 3D immersive platform with tens of millions of daily active users, where a global community of developers and creators build and monetize experiences. The company is publicly traded (NYSE: RBLX) and has been aggressively investing in AI-driven creation tools, generative content, and avatar/NPC intelligence as core growth vectors. In the last 12–24 months Roblox has publicly emphasized AI infrastructure buildout — including GPU fleet expansion and model serving at scale — to power features like AI-assisted building, moderation, and personalization. Engineering reputation is generally strong for large-scale distributed systems, real-time networking, and game engine work, though the AI infrastructure org is relatively newer and scaling rapidly. Specific internal project names, team structures, and named leaders are not confirmed here.
Team stack
Based on the JD, the Compute Platform team operates a hybrid on-prem + public cloud (likely AWS and/or GCP based on JD language) GPU/CPU fleet managed via unified Fleet APIs. Core stack almost certainly includes: Managed Kubernetes (custom Roblox Kubernetes Service with bespoke Operators, Controllers, CRDs), GPU scheduling layers (likely NVIDIA device plugins, topology-aware schedulers — possibly custom or Volcano/Kueue-based), container runtimes, and cloud-native networking (CNI plugins, service mesh — likely Istio or Envoy-based). AI workload side likely includes distributed training frameworks (PyTorch DDP/FSDP, possibly Megatron or NeMo) and inference serving (likely Triton, vLLM, or similar). Driver/firmware fleet management tooling is explicitly called out. Data analytics and storage workloads also run on this platform. Observability stack unknown but likely Prometheus/Grafana or similar. All inferences marked 'likely' or 'based on the JD' where unconfirmed.
Likely questions (10)
| area | question | why |
|---|---|---|
| system_design | Design a GPU fleet health and readiness validation system that prevents scheduling jobs onto unhealthy nodes before compute cycles are wasted. Walk through detection, remediation, and the operator/controller pattern you'd use. | JD explicitly calls out 'validate resource readiness before scheduling to avoid wasted compute cycles' and ownership of driver/firmware management and fleet-wide health — this is a core product surface. |
| system_design | How would you architect a Kubernetes-based multi-tenant GPU scheduling system that balances training jobs (long-horizon, topology-sensitive) against inference workloads (latency-sensitive, bursty) on the same fleet? | JD requires deep Kubernetes internals knowledge and GPU topology-aware placement/preemption — this tests whether the candidate can reason about the core scheduling tradeoffs at the heart of the role. |
| domain | Walk me through the lifecycle of a Custom Resource Definition (CRD) and Operator you've shipped or deeply influenced. What were the API design tradeoffs, and how did you handle versioning and backward compatibility? | JD specifically calls out 'productizing custom Kubernetes Operators, Controllers, and CRDs' as a hard requirement — they want evidence of hands-on depth, not just familiarity. |
| domain | Roblox runs training and inference for frontier models on the same physical fleet. How do you think about cost-to-serve optimization — what levers exist at the compute platform layer versus the model/application layer? | JD explicitly states 'optimizing cost-to-serve' for GPU infrastructure supporting training and inference — this is a key strategic responsibility of the role. |
| behavioral | Tell me about a time you had to align seven or more platform teams with competing priorities around a shared infrastructure primitive. How did you build consensus and make tradeoff decisions? | JD says 'partner closely with seven key platform teams' — cross-functional alignment at this scale is explicitly called out as a core competency. |
| behavioral | Describe a situation where you drove a platform reliability initiative — specifically around reducing mean-time-to-detection or recovery. What did you instrument, what did you ship, and what was the outcome? | JD calls out 'Compute Platform Reliability' and 'lowering mean-time-to-detection and recovery' as a named priority — they want a concrete track record here. |
| coding | You need to write a simple Kubernetes controller reconciliation loop in pseudocode or Python that watches a custom GPU resource, checks node health via a mock API, and cordons unhealthy nodes. Walk through your logic. | JD requires deep Kubernetes internals and Operator/Controller experience — a light coding/design exercise tests whether the PM can engage credibly with engineers at the implementation level. |
| domain | How would you approach building a unified Fleet API abstraction that works across on-prem GPU clusters and multiple public clouds (AWS, GCP) without leaking cloud-specific primitives into the developer-facing surface? | JD explicitly describes 'fleet of GPU and CPU machines managed via unified Fleet APIs — all across on-prem and cloud' as a core product surface the candidate will own. |
| culture | Roblox's primary users of this platform are AI researchers and product engineers, not end consumers. How do you approach developer experience and 'time to first successful job' as a product metric for an infrastructure platform? | JD emphasizes 'developer experience designed-in from day one' and a 'builder mindset' — Roblox's platform serves internal developers and the candidate's philosophy here signals cultural fit. |
| behavioral | You've built 0-to-1 products as a founder. How do you recalibrate your operating style when the product is a deeply technical infrastructure platform inside a large company with existing stakeholders, compliance requirements, and legacy systems? | The candidate's background is heavily founder/0-to-1 — interviewers will probe whether they can operate effectively inside a scaled engineering org with existing constraints and partner dependencies. |
Talking points
- At Intuit, I owned the ICE platform end-to-end — scaling throughput from 6K to 50K TPS via rSocket migration supporting ~1.5M concurrent connections with sub-25ms TP99, and reducing developer onboarding from 2–3 weeks to minutes. That's the exact reliability + developer experience + scale trifecta the Roblox Compute Platform JD describes.
- I built a production RL post-training workbench (RL Workbench, 2026) that benchmarks GRPO/DPO across TRL, VeRL, OpenRLHF, and NeMo RL with GPU Docker passthrough and live SSE metric streaming on Apple Silicon MPS and CUDA — giving me direct hands-on fluency with the GPU training infrastructure and framework-level tradeoffs that Roblox's AI workloads depend on.
- My aeval platform (FastAPI orchestrator, TimescaleDB, Redis job queue, Ollama) and the BRAIN protein structure prediction platform (PyTorch, MLflow, Optuna, Docker orchestration across 6 containers, 823 automated tests) demonstrate I can ship production-grade AI infrastructure with rigorous observability and testing — not just prototype it.
- At Intuit I conducted an enterprise-wide Service Language Assessment across 9 languages and built Asterias, a declarative asset lifecycle management platform with a GraphQL API — evidence that I can reason about platform abstractions and make strategic investment decisions that affect hundreds of internal developers, directly analogous to Roblox's Fleet API and Kubernetes platform strategy.
- As a NeurIPS-published researcher (protein structure prediction, 2014) who hand-coded BPTT in C++ in 2004 and has since implemented 12 RL algorithms including PPO, GRPO, and DPO, I can engage with Roblox's AI researchers as a credible technical peer — not just a PM translating requirements — which is critical for a role where the compute platform directly enables frontier model training and inference.