Principal Engineer, AI Ecosystem

About the role

Google Cloud accelerates organizations' ability to digitally transform their business with the best infrastructure, platform, industry solutions and expertise. We deliver enterprise-grade solutions that leverage Google’s technology – all on the cleanest cloud in the industry. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.

Responsibilities

• Enable massive scale so that ML engineers working primarily in Python can iterate locally and seamlessly scale to 1,000,000+ accelerators without needing to become experts in infrastructure.
• Act as a proxy for the emerging AI/ML end-user persona, evolving the GKE team's intuition and empathy for real-world problems and opportunities within the AI workload lifecycle.
• Drive the development and architectural outlook of a substrate optimized for Accelerated and Agentic Workloads.
• Imagine, architect, and lead the technical execution of industry-defining standards through both direct, direct technical work and by mentoring and guiding teams of engineers.

Requirements

• Bachelor’s degree in Computer Science, a related technical field, or equivalent practical experience.
• 15 years of experience in software engineering, focusing on technical innovation, large-scale systems operations, or engineering leadership.
• 10 years of experience working with distributed systems, ML/AI infrastructure, deep learning frameworks, or large-scale compute orchestration.

Qualifications

• Master’s degree or PhD in Computer Science, Artificial Intelligence, High-Performance Computing, or a related field.
• Experience operating at a company-wide or multi-organization level, driving technical direction that influences foundational systems.
• Expertise in HPC, distributed workload schedulers (e.g. Slurm), and managing complex LLM training and inference workloads at massive scale.
• Familiarity with cloud-native orchestration frameworks and an understanding of how to bridge traditional HPC methodologies with modern cloud infrastructure.
• Ability to influence outside lines of formal authority, working effectively within highly matrixed environments to align technical and product strategies.

Skills

• Deep empathy and technical understanding of the AI end-user.
• Domain expertise in AI/ML frameworks.
• Technical leadership and building out the team's intuition for AI workloads.
• Technical bridge between GKE's robust infrastructure and the rapidly evolving OSS AI ecosystem (e.g., Ray, Slurm, KubeFlow, PyTorch, NumPy, CUDA).

Benefits

• Health, dental, vision, life, disability insurance.
• Retailment Benefits: 401(k) with company match.
• Paid Time Off: 20 days of vacation per year, accruing at a rate of 6.15 hours per pay period for the first five years of employment.
• Sick Time: 40 hours/year (increased to 69 hours/year for Seattle) including 5 discretionary sick days per instance.
• Maternity Leave (Short-Term Disability + Baby Bonding): 28-30 weeks.
• Baby Bonding Leave: 18 weeks.
• Holidays: 13 paid days per year.

Pay

US: $307000 - $428000 (USD) + 30% bonus target + equity + benefits

Schedule

Not specified