Engineering Manager: Cloud & Distributed Systems

Polluxa, Inc.; Pittsburgh (Allegheny County); On-site; USD 180,000 - 240,000; Full time

Job summary

Polluxa, Inc. seeks a senior engineering leader to grow technical talent and drive strategy across distributed systems and storage infrastructure in Pittsburgh.

You will mentor engineers, own performance trajectories, and partner with global teams to align local execution with multi-site objectives while shaping core primitives with shared storage capabilities for scalable ML workloads.

Qualifications

• Bachelor's degree or equivalent practical experience.
• 8 years of experience in software engineering, management, and distributed systems.
• Experience in people management.
• Experience with cross-functional collaboration.

Responsibilities

• Develop individual contributors into technical leaders and domain owners, guide performance reviews, promotion trajectories, and career advancement.
• Serve as the local anchor and culture carrier for the Pittsburgh site, aligning local execution with global multi-site strategy and cross-functional partners.
• Partner with technical leads to componentize the stack, aligning our core primitives with shared storage infrastructure across the broader organization.
• Evolve systems capable of supporting data growth and intensive write fan-in from distributed training jobs and continuous logging.
• Collaborate with premier customer engineering teams (e.g., Gemini Pre-Training, DeepMind, Ads ML, Search, YouTube, Waymo) to translate emerging ML workload patterns into core storage capabilities.

Skills

Software engineering

Management

Distributed systems

People management

Cross-functional collaboration

Education

Bachelor's degree or equivalent practical experience

Job description

Polluxa, Inc. seeks a senior engineering leader to grow technical talent and drive strategy across distributed systems and storage infrastructure in Pittsburgh.

You will mentor engineers, own performance trajectories, and partner with global teams to align local execution with multi-site objectives while shaping core primitives with shared storage capabilities for scalable ML workloads.