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Staff Product Security Engineer Jobs

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Explore current staff product security engineer jobs. Use filters to narrow by work mode, employment type, experience and date posted.

S
Snowflake
πŸ“ Menlo Parkβ€’ Full-time
1mo ago

At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset β€” who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done. About the role We are an AI-first analytics team. We don't use AI to augment traditional BI workflows β€” we've replaced them. The Finance Analytics team builds the intelligence layer that Strategic Finance runs on: AI agents that encode repeatable finance processes, Streamlit apps that surface real-time insight, semantic models that let any analyst query complex data in plain English, and workflow automations that collapse hours of manual work into a single prompt. Our primary development environment is CoCo (Cortex Code), Snowflake's AI coding assistant, and SnowWork , the AI IDE we ship work in. Every deliverable on this team is built AI-first: you design the workflow, you write the prompt, you validate the output. If you are still building dashboards by hand, refreshing Excel files manually, or treating AI as a spell-checker for your code β€” this role will ask you to operate differently. This is a high-breadth seat. One week you're building a new AI agent for quarterly revenue analysis; the next you're designing a sensitivity analysis tool for an earnings war room. You are equally comfortable in an AI-IDE, a Python file, and a stakeholder summary for a senior finance leader. What you'll work on AI agent and workflow development (primary focus) Design and build skills and a

pythonsqlgit
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B
Biohub
πŸ“ Redwood Cityβ€’ Full-timeβ€’ Hybridβ€’ $241K – $331K/yr
1mo ago

Biohub is the first large-scale initiative bringing frontier AI models, massive compute, and frontier experimental capabilities under one roof. We're building a general-purpose system to accelerate scientific discovery, integrating frontier AI models, biological foundation models, and lab capabilities, with the ultimate goal of curing disease. Our technology powers scientists around the world, translating AI capabilities into tools that accelerate research everywhere. The Team The AI Cluster Production Engineering team is part of the AI Compute Platform organization at Biohub, a non-profit research lab committed to open science and open-source AI. We own the design, operation, and reliability of large-scale multi-GPU AI clusters that power frontier AI biology research: protein language models, genomic foundation models, and scientific reasoning systems built to be shared, not monetized. Our clusters run Slurm on Kubernetes infrastructure and support everything from day-to-day AI researcher workflows to multi-node hero training runs at thousands of GPUs. The team works at the intersection of AI tooling, distributed systems, HPC, and frontier AI, debugging deep AI infrastructure problems and building AI systems critical to the entire AI organization. The Opportunity CZ Biohub's mission is to cure or prevent all human disease. Achieving that requires training frontier-scale AI biology models, and that demands reliable, high-performance compute infrastructure. This is production engineering work at a frontier AI lab, with the twist that the mission is biology and the science is open. You'll keep GPU clusters running at high utilization, debug the toughest distributed systems failures, and build the operational foundations for scaling to multi-thousand GPU hero runs. The technical problems are genuinely hard (e.g., multi-node distributed training, InfiniBand fabrics, large-scale storage, Slurm at scale) inside an organization where the work is aimed at helping peop

pythonkubernetesgit
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