Software Engineer II, ML Ops, tvScientific

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