Engineering Manager, Cloud Inference AWS

Polluxa, Inc.; San Francisco (CA); On-site; USD 180,000 - 270,000; Full time

Job summary

Polluxa, Inc. seeks an Engineering Manager to lead the Cloud Inference team for AWS, owning Claude on AWS end-to-end from API to deployment. You will drive scale, performance, safety and security while advancing packaging, testing and deployment infrastructure globally.

You will set strategy, hire and coach high-caliber engineers, collaborate across teams, and travel frequently between Seattle and the SF Bay Area to align with partners and stakeholders in a fast-paced AI space.

Qualifications

• 10+ years in high-scale software development, infrastructure or capacity management.

Responsibilities

• Set technical strategy and oversee development of Claude on AWS across all layers of the technical stack.
• Collaborate with teams to understand product, infrastructure, operations and capacity needs.
• Work with stakeholders to align on goals and drive outcomes.
• Create clarity in a fast-changing environment.
• Hire and coach top technical talent, supporting a high-performing team.
• Design and run postmortem reviews, incident response, and on-call rotations.

Skills

High-scale infra

Capacity mgmt

Engineering mgmt

Recruiting

Scaling ops

AI safety focus

Stakeholder alignment

10+ yrs exp

Tools

GPUs

TPUs

NCCL

Trainium

Job description

About the Role

We are seeking an experienced Engineering Manager to lead the Cloud Inference team for AWS. You will lead your team to scale and optimize Claude to serve the massive audiences of developers and enterprise companies using AWS. You will own the end-to-end product of Claude on AWS, including API, load balancing, inference, capacity and operations. Your team will ensure our LLMs meet rigorous performance, safety and security standards and enhance our core infrastructure for packaging, testing, and deploying inference technology across the globe. Your work will increase the scale at which Anthropic operates and accelerate our ability to reliably launch new frontier models and innovative features to customers across all platforms.

Responsibilities:

• Set technical strategy and oversee development of Claude on AWS across all layers of the technical stack.
• Collaborate across teams and companies to deeply understand product, infrastructure, operations and capacity needs, identifying potential solutions to support frontier LLM serving
• Work closely with cross-functional stakeholders across companies to align on goals and drive outcomes
• Create clarity for the team and stakeholders in an ambiguous and evolving environment
• Take an inclusive approach to hiring and coaching top technical talent, and support a high performing team
• Design and run processes (e.g. postmortem review, incident response, on-call rotations) that help the team operate effectively and never fail the same way twice

You may be a good fit if you:

• Have 10+ years of experience in high-scale, high-reliability software development, particularly infrastructure or capacity management
• Have 5+ years of engineering management experience
• Experience recruiting, scaling, and retaining engineering talent in a high growth environment
• Have experience scaling products, resources and operations to accommodate rapid growth
• Are deeply interested in the potential transformative effects of advanced AI systems and are committed to ensuring their safe development
• Excel at building strong relationships and strategy with stakeholders across engineering, product, finance, and sales
• Have experience working with external partners to align goals and deliver impact
• Enjoy working in a fast-paced, early environment; comfortable with adapting priorities as driven by the rapidly evolving AI space
• Have excellent written and verbal communication skills
• Demonstrated success building a culture of belonging and engineering excellence
• Are motivated by developing AI responsibly and safely
• Are willing and able to travel frequently between Seattle and the SF Bay Area

Strong candidates may also have experience with:

• Experience with machine learning infrastructure like GPUs, TPUs, or Trainium, as well as supporting networking infrastructure like NCCL
• Experience as a Product Manager
• Experience with deployment and capacity management automation
• Security and privacy best practice expertise