Technical Program Manager, Inference Performance

About Anthropic

Anthropic’s mission is to create reliable, interpretable, and steerable AI systems, aiming for safe and beneficial AI for users and society. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

About The Role

As a Technical Program Manager, Inference Performance, you will be the critical bridge between our inference systems and the broader organization. This role involves driving strategic initiatives across inference runtime and accelerator performance, coordinating model launches, managing cross-platform dependencies, and ensuring reliability across multiple hardware targets. It is essential for keeping our most contended infrastructure teams shipping effectively, as Research, Product, and Safety all depend on their output.

Responsibilities

• Systems Integration & Coordination: Lead cross-functional initiatives for new infrastructure integration, establishing clear ownership, timelines, and communication channels. Drive end-to-end planning for major infrastructure transitions, including platform modernization and new tech adoption.
• Performance & Efficiency: Partner with engineering teams to identify optimization opportunities, track performance metrics, and prioritize work that unlocks capacity gains. Coordinate across runtime and accelerator layers to ensure efficiency wins ship without compromising reliability.
• Launch Coordination: Drive end-to-end readiness for model and feature launches across multiple hardware platforms. Establish processes for cross-platform validation, manage launch timelines, and ensure smooth handoffs between runtime, accelerator, and downstream teams.
• Strategic Planning: Own and prioritize the inference deployment roadmap, working closely with engineering leadership to prioritize initiatives and manage dependencies. Provide visibility into upcoming changes and their organizational impact.
• Stakeholder Communication: Build strong relationships across research, engineering, and product teams to understand requirements and constraints. Translate technical complexities into clear updates for leadership and ensure alignment on priorities and timelines.
• Process Improvement: Identify inefficiencies in current workflows and drive systematic improvements. Establish metrics and dashboards to track infrastructure health, capacity utilization, and deployment success rates.

You May Be a Good Fit If You

• Have several years of experience in technical program management, with proven success delivering complex infrastructure programs, preferably in ML/AI systems or large-scale distributed systems.
• Have deep technical understanding of inference systems, compilers, or hardware accelerators to engage substantively with engineers and identify technical risks.
• Excel at creating structure and processes in ambiguous environments, bringing clarity to complex cross-team initiatives.
• Have strong stakeholder management skills and can build trust with both technical and non-technical partners.
• Are comfortable navigating competing priorities and using data to drive technical decisions.
• Have experience with infrastructure scaling initiatives, hardware integrations, or deployment governance.
• Thrive in fast-paced environments and can balance strategic planning with tactical execution.
• Are passionate about AI infrastructure and understand the unique challenges of deploying and scaling large language models.

How We're Different

At Anthropic, we believe that the highest-impact AI research will be big science. We work as a single cohesive team on a few large-scale research efforts, valuing impact in advancing our long-term goals of steerable, trustworthy AI. We view AI research as an empirical science, emphasizing collaboration and frequent discussions to pursue high-impact work. Our research directions include GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.

Key skills/competency

• Technical Program Management
• ML/AI Systems
• Distributed Systems
• Inference Performance
• Hardware Accelerators
• Cross-functional Leadership
• Strategic Planning
• Process Improvement
• Stakeholder Management
• Data-driven Decision Making