Member of Technical Staff - Responsible AI (CoreAI)

About the role

A builder who lives and breathes AI—you regularly prototype, experiment, or ship features using modern AI tools, frameworks, and model APIs. Experience working with generative AI systems (e.g., LLMs, diffusion models, multimodal models), including prompt engineering, evaluation, or fine‑tuning. Hands‑on experience with AI-assisted coding tools (e.g., GitHub Copilot, Cursor, Claude Code).

Familiarity with Responsible AI and security concepts or strong interest in learning them deeply. Experience working in fast‑paced, experimental environments (startups, prototypes, or incubation teams).

Comfort with taking ideas from ambiguous beginnings to production, iterating fast with customer insights Bachelor's Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience. Demonstrated track record of high-impact innovation, open-source contributions or publications.

Experience working on safety, alignment, or Responsible AI. 6+ years of technical engineering experience designing and delivering highly available, large-scale cloud services. 4+ years of technical engineering experience with machine learning models. Ability to navigate the company, and influence and inspire peers in engineering and broad product development.

Prototype, build, and iterate on new Responsible AI tools and capabilities that make it simple for developers to build and deploy AI responsibly at scale. Apply subject-matter expertise in cross-product features, collaborating with appropriate stakeholders to drive project plans, release plans, and deliverables across multiple groups.

Proactively seek out new knowledge and adapt to new trends, technical solutions, and patterns that improve the availability, reliability, efficiency, observability, and performance of products while also driving consistency in monitoring and operations at scale, and share knowledge with other engineers.

Work effectively in a fast-paced product environment where rapid prototyping, tight feedback loops, and iterative learning are core to how we build.