Engineering Manager, Accelerator Platform

Responsibilities:- Build and lead the Accelerator Platform team -- hiring, developing, and retaining engineers who thrive at the hardware/software boundary- Own the end-to-end bring-up lifecycle for new accelerator platforms (multiple generations of Trainium, TPUs, and GPUs), from initial silicon availability through production-ready inference- Define and drive the platform normalization layer -- ensuring new hardware integrates cleanly with Anthropic's inference serving stack to provide a consistent abstractio- Partner with cloud providers (AWS, GCP, Microsoft Azure) and chip vendors on hardware roadmaps, capacity planning, and platform-specific technical challenges- Collaborate closely with teams across Inference and Infrastructure to ensure new platforms meet production reliability and latency requirements from day one- Contribute to Anthropic's multi-cloud compute strategy -- helping the organization maintain optionality across accelerator families and avoid lock-in to any single vendor- Manage the team's priorities across competing demands: new platform bring-up, ongoing production support for existing platforms, and longer-term investments in tooling and automation.You may be a good fit if you:- Have significant experience managing infrastructure or platform engineering teams (3+ years in engineering management)- Have deep technical fluency in systems programming, distributed systems, or hardware/software co-design -- you need to understand the stack deeply enough to make sound technical and hiring decisions- Have experience bringing up or operating heterogeneous compute infrastructure at scale -- whether that's GPU clusters, TPU pods, custom ASICs, or FPGA deployments.- Are comfortable with ambiguity and can build structure where none exists. This team is being carved out as a new entity; you'll be defining its charter, processes, and culture from scratch- Think strategically about hardware roadmaps and can translate vendor capabilities into engineering plans- Build strong cross-functional relationships -- this role requires tight collaboration with hardware vendors, cloud partners, and half a dozen internal teams- Care deeply about both technical excellence and the people doing the work.Strong candidates may also:- Have direct experience with ML accelerator architectures (GPU/CUDA, TPU/XLA, Trainium/Neuron, or similar)- Have worked on ML inference serving infrastructure at scale (1000+ accelerators)- Have experience with Kubernetes-based ML workload orchestration- Understand ML-specific networking (RDMA, InfiniBand, NVLink, ICI) and how interconnect topology affects serving performance- Have experience managing vendor relationships and influencing hardware/software roadmaps- Have led teams through rapid growth phases (hiring 5+ engineers in a short timeframe).The annual compensation range for this role is listed below.For sales roles, the range provided is the roles On Target Earnings (

Created: 2026-02-23