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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 role's On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.

Annual Salary:

$405,000 - $485,000 USD

Logistics

Education requirements: *We require at least a Bachelor's degree in a related field or equivalent experience. *Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every