Engineering Manager, Launch & Sandboxes- Weights & Biases

Weights & Biases•Sunnyvale, CA

5d•$165,000 - $242,000•Hybrid

About The Position

You'll be joining the ML Workflows organization as the Engineering Manager for the Launch & Sandboxes team. Our team owns two interconnected product areas: Sandboxes, W&B's cloud execution environments that let ML practitioners run code in isolated, GPU-enabled containers directly from the W&B platform, and Launch, which handles job launching, compute orchestration, and integration with HPC schedulers. We build the infrastructure that connects W&B's experiment tracking and model development tools to the actual compute where ML work happens, spanning Kubernetes clusters, HPC nodes, and cloud VMs. As the Engineering Manager for Launch & Sandboxes, you'll be involved in the full product lifecycle, from ideation and roadmap shaping through delivery and being available for customers. Your day-to-day will involve partnering with product management to prioritize across competing initiatives, unblocking your engineers on cross-team dependencies, and ensuring the team ships reliably while managing technical debt. You'll need to be technically fluent enough to participate in architecture discussions around container orchestration, distributed authentication, and billing infrastructure, and you're not afraid to get your hands dirty when the team needs it. At the same time, your primary focus is growing your engineers into autonomous leaders and technical decision-makers. We're an AI-forward organization that embraces LLM-based coding tools and agentic workflows to accelerate development, and we'd expect you to champion that culture within your team. This role carries a heavy customer and user focus: you'll stay close to how ML practitioners use the product and make sure that perspective drives your team's priorities. Like any role at a hyperscaler, this comes with high accountability and ownership. You'll own your team's outcomes end-to-end, from roadmap commitments through production reliability. We expect engineering managers to make hard prioritization calls, hold the bar on engineering quality, and build teams that operate with autonomy and urgency.

Requirements

3+ years of engineering management experience, with a track record of shipping infrastructure or platform products
5+ years of software engineering experience prior to management, with backend systems depth
Technical fluency in Go, Python, and Kubernetes (you won't write code daily, but you need to review PRs, unblock architectural decisions, and understand production incidents)
Experience managing teams that own distributed systems in production
Demonstrated ability to balance product delivery with technical health (managing tech debt, reliability, on-call)
Experience partnering with product managers to shape and prioritize a roadmap
Track record of growing engineers across levels
Strong communication skills with both engineering and non-engineering stakeholders
Comfortable operating in ambiguity, especially in newer product areas where scope is still being defined

Nice To Haves

Experience with ML infrastructure, ML platforms, or developer tools for data scientists
Familiarity with compute orchestration, job scheduling, or container isolation technologies
Experience managing teams through a product area transition (e.g., early-stage to growth, or post-acquisition integration)
Background in HPC or scientific computing environments
Experience with billing systems, metering, or usage-based pricing infrastructure
Founding experience or early-stage background where you took a product from idea to the hands of users

Responsibilities

Partnering with product management to prioritize across competing initiatives
Unblocking your engineers on cross-team dependencies
Ensuring the team ships reliably while managing technical debt
Participate in architecture discussions around container orchestration, distributed authentication, and billing infrastructure
Growing your engineers into autonomous leaders and technical decision-makers
Stay close to how ML practitioners use the product and make sure that perspective drives your team's priorities
Own your team's outcomes end-to-end, from roadmap commitments through production reliability
Make hard prioritization calls
Hold the bar on engineering quality
Build teams that operate with autonomy and urgency