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About the Role
Fleet Management builds the automation layer for Meta's data center fleet, owning systems that plan, orchestrate, and execute operational workflows for hundreds of thousands of machines. We focus on planned maintenance, repair, fleet lifecycle operations, safety, capacity control, and autonomy, building large-scale workflow orchestration engines to ensure fleet operations scale efficiently.
Responsibilities
- Own technical direction for a significant problem area of fleet automation, defining architecture, product direction, and driving execution.
- Design and evolve orchestration engines, including workflow modeling, scheduling, conflict resolution, and execution state machines.
- Ensure large-scale automation safety through guardrails like unavailability budgets, rate limiting, graceful cancellation, and blast-radius control.
- Lead through others by decomposing broad areas into workstreams, reviewing designs, and serving as the technical owner for key decisions.
- Drive cross-functional alignment with teams like capacity management, provisioning, remediation, and hardware operations.
- Replace manual processes and one-off scripts with scalable platforms and abstractions to reduce operational load.
- Raise the engineering bar through mentoring, setting standards, and providing technical framing for roadmap discussions.
Requirements
- BS/MS in Computer Science or equivalent practical experience.
- 8+ years of professional software engineering experience, with significant time as a technical owner of a large production system.
- Demonstrated staff-level scope, leading design and delivery of systems spanning multiple teams and planning cycles with measurable impact.
- Deep distributed systems expertise in areas like workflow/orchestration engines, control planes, scheduling, state machines, or resource management.
- Proficiency in Python, C++, Java, Rust, or Go, demonstrated through building and maintaining production systems.
- Track record of operating critical systems, including on-call ownership, incident leadership, observability, SLO design, and reliability improvement.
- Ability to lead ambiguous, cross-organizational work to completion.
- Clear written communication skills for both engineers and senior leadership.
- Experience growing other engineers through mentoring, design review, and technical direction-setting.
Skills
- Workflow or orchestration engines
- Control planes
- Scheduling
- State machines
- Resource management systems
- Python
- C++
- Java
- Rust
- Go
- Oncall ownership
- Incident leadership
- Observability
- SLO design
- Reliability improvement
- Constraint-based scheduling
- Planning or solver-backed systems
- AI tools integration
- AI/agentic automation for operational workflows
- Infrastructure automation
- Fleet or capacity management
- Hardware lifecycle
- Data center operations
- Cloud infrastructure
- Responsible AI practices
- Prompt/context engineering
- Agent orchestration
- Large mature codebase navigation
- Cross-team dependency management
- Job execution engines
- Legacy system consolidation/deprecation
- Heterogeneous external infrastructure APIs
- Public cloud provider lifecycle and maintenance models
Experience Level
- 8+ years of professional software engineering experience
- Staff-level scope
Education Level
- BS/MS in Computer Science or equivalent practical experience
About the Company
- Fleet Management builds the automation layer for Meta's data center fleet.
- We own the systems that plan, orchestrate, and execute the operational workflows that keep hundreds of thousands of machines healthy, available, and efficient across every region, and increasingly across public cloud providers as well.
- Our platforms cover the full lifecycle of work performed on the fleet: Planned maintenance and rollouts, Unplanned work and repair, Fleet lifecycle operations, Safety and capacity control, and Autonomy.
- Practically, this means we build large-scale workflow orchestration engines: systems that model operational intent, schedule it against fleet constraints, execute it across dozens of downstream systems, and decide what to do when steps fail.
- The engineering problems are distributed systems problems - state machines, scheduling, conflict resolution, idempotency, partial failure, and strong safety guarantees over irreversible physical actions.
- The fleet is growing fast, and the operational volume with it.
- Our mandate is to make fleet operations scale by an order of magnitude without a corresponding increase in human effort.