Staff Software Engineer - Agentic AI & Trust Safety

Google; Kirkland (WA); On-site; USD 207,000 - 300,000; Full time

Benefits offered by this job

Health insurance

Retirement plan (401(k))

Paid time off

Sick leave

Maternity leave

Baby bonding leave

Paid holidays

Job summary

Google is seeking a senior software engineer to lead Trust and Safety initiatives, architect next-generation AI/ML systems, and ensure scalable, fault-tolerant distributed infrastructure across Google-scale environments.

The role requires deep experience in distributed systems, ML infrastructure, and cross-functional leadership, with opportunities to influence product strategy and engineering standards. Strong problem solving and mentoring abilities are essential.

Qualifications

• Bachelor’s degree or equivalent practical experience.
• 8 years of experience with designing and implementing large-scale distributed systems.
• 5 years of experience with ML infrastructure or another ML field.
• Experience with architectural ownership for distributed systems or infrastructure components.
• Experience building and deploying agentic AI systems.

Responsibilities

• Define, advocate, and execute the Trust and Safety technology roadmap, architecting AI/ML systems and reliable distributed infrastructure.
• Oversee high-availability, low-latency production systems with SLO guarantees and cost-efficiency.
• Lead multi-team initiatives from discovery to large-scale deployment across parallel workstreams.
• Define fault-tolerant architectures and mentor tech leads on code quality, CI/CD, and testing.
• Collaborate with Product, Policy, and Data Science to align tech strategy with business goals.

Skills

Distributed systems design

ML infrastructure

Architectural ownership

Agentic AI systems

Education

Bachelor’s degree

Master’s degree or PhD preferred

Job description

Google is seeking a senior software engineer to lead Trust and Safety initiatives, architect next-generation AI/ML systems, and ensure scalable, fault-tolerant distributed infrastructure across Google-scale environments.

The role requires deep experience in distributed systems, ML infrastructure, and cross-functional leadership, with opportunities to influence product strategy and engineering standards. Strong problem solving and mentoring abilities are essential.