Pinterest is a platform where millions find creative ideas and inspiration. They are seeking a Software Engineer II for their tvScientific department to work on AI workflows, improve developer experience, and enhance observability tooling in a fast-paced environment.
Responsibilities:
- Contribute to the infrastructure supporting AI workflows — training pipelines, Kubernetes deployments and CI/CD
- Help improve the developer experience for the data science team — small frictions add up, and you’ll help eliminate them
- Build out and improve observability tooling — learning to see the system clearly is a core skill we’ll develop together
- Keep deployments clean and correct as the platform evolves
- Grow into a deeper technical contributor under the mentorship of senior engineers who have done this at high scale
Requirements:
- A genuine, demonstrable depth in Linux — hands-on experience beyond basic usage (for example, debugging, configuration or performance tuning)
- Strong software engineering fundamentals — you write clean code, reason about systems and debug methodically
- A systems-oriented mindset — you think about why things work, not just that they work
- Early exposure to reliability concepts — CI/CD, infrastructure-as-code or similar
- An ownership mindset — especially when diagnosing and resolving production or project issues
- 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)
- High integrity and ownership: you protect sensitive data, avoid over-reliance on AI and remain accountable for final decisions and deliverables
- 2+ years of experience building and operating high-performance distributed systems
- Bachelor's degree in computer science, engineering, a related field or equivalent experience
- Experience with NixOS or other tools for reproducible builds, and an interest in making development environments predictable and reliable
- Experience with Zig or similar low-level languages, and curiosity about what your compiler and runtime are doing under the hood
- You've reverse-engineered something — a protocol, a binary, a game, etc
- You've deployed something real to Kubernetes, even if it was a homelab
- Experience with Terraform or other infrastructure-as-code tools in a real context
- Exposure to adtech, CTV or other high-performance/low-latency environments
- Python or Scala experience in a data-adjacent context