Original listing text, shown exactly as published by the company.
What you’ll do
- Own the infrastructure that supports AI model training, deployment and serving — from Kubernetes clusters to CI/CD pipelines.
- Co-architect our next-generation data processing pipeline for reliability, robustness and observability.
- Drive developer experience improvements for the data science team, building direct relationships with the people you’re unblocking.
- Build and refine observability across the stack so we can detect, debug and resolve issues quickly.
- Evolve our tooling and workflows as the system grows, with care for correctness and performance at every layer.
What we’re looking for
- Linux systems knowledge — beyond surface level, with experience digging into behavior, configuration or source.
- Strong software engineering fundamentals paired with reliability/platform engineering experience (for example, CI/CD, infrastructure-as-code, distributed systems).
- Experience working in high-performance environments such as RTB, HFT, hard real-time systems or similar.
- Strong observability skills — you build and use dashboards, alerts and traces to understand complex systems.
- A systems-oriented mindset that spans multiple layers of the stack.
- An ownership mindset and high integrity: you’re accountable for what ships and you protect sensitive data.
- Experience in mentoring other engineers on the team and leading projects, and helping set the technical bar for the team.
- Comfort using AI tooling to accelerate your work, with the discipline to verify what it produces.
- Demonstrated ability to use AI to improve speed and quality in your day-to-day workflow for relevant outputs.
- A track record of critically evaluating and validating AI-assisted work (for example, testing, source checking, data validation, peer review).
- 4+ years of experience building and operating high-performance distributed systems.
- Bachelor’s degree in computer science, engineering, a related field or equivalent experience.
- Nice-to-haves
- Reverse-engineering experience (for example, protocols, binaries or hypervisors).
- Experience with NixOS or other tools for reproducible builds and system configuration.
- Experience with Zig or similar low-level languages, and curiosity about what your compiler and runtime are doing under the hood.
- Experience working with large-scale data systems.
- Terraform, EKS or other MLOps tooling in production.
- Exposure to adtech, CTV or other high-performance/low-latency environments.
- Experience deploying distributed systems in multi-cloud environments.
- Experience with sub-10ms hard real-time systems.
In-Office Requirement Statement
- We recognize that the ideal environment for work is situational and may differ across departments. What this looks like day-to-day can vary based on the needs of each organization or role.
Relocation Statement
- This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.
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At Pinterest we believe the workplace should be equitable, inclusive, and inspiring for every employee. In an effort to provide greater transparency, we are sharing the base salary range for this position. The position is also eligible for equity. Final salary is based on a number of factors including location, travel, relevant prior experience, or particular skills and expertise.
