Senior Staff Software Engineer, AI Infrastructure

Company Description

LinkedIn is the world's largest professional network, built to create economic opportunity for every member of the global workforce. Our products help people make powerful connections, discover exciting opportunities, build necessary skills, and gain valuable insights every day. We're also committed to providing transformational opportunities for our own employees by investing in their growth. We aspire to create a culture that's built on trust, care, inclusion, and fun – where everyone can succeed.

Join us to transform the way the world works.

Job Description

At LinkedIn, our approach to flexible work is centered on trust and optimized for culture, connection, clarity, and the evolving needs of our business. The work location of this role is hybrid, meaning it will be performed both from home and from a LinkedIn office on select days, as determined by the business needs of the team. 

Join us in building the AI Governance Platform that ensures LinkedIn’s Models are ready and safe to operate in production.

LinkedIn’s AI Governance Platform sits in the critical path between model development and production deployment. It serves as a centralized control layer in the deployment path for machine learning models across recommendations, search, ads, GenAI, and other AI-powered experiences, helping determine whether models are ready to move into production.

The team builds large-scale, metadata-driven distributed systems that capture signals throughout the ML lifecycle, process high volumes of model and system metadata, execute model validation and evaluation workflows, and apply automated policy-based guardrails before models reach production. These systems enable LinkedIn to consistently enforce standards around fairness, bias, privacy, security, safety, and overall model quality.

The platform must operate at the speed and scale of LinkedIn’s ML ecosystem, supporting continuous model training, evaluation, and high-velocity deployment while translating evolving AI governance requirements into scalable, repeatable, and enforceable engineering capabilities.

As a Sr. Staff Software Engineer, you will help define the architecture and technical strategy for LinkedIn’s next generation of AI Governance infrastructure. You will solve complex distributed systems, metadata management, and ML infrastructure problems while influencing how trusted and compliant AI practices are implemented across the company.

You will partner closely with ML engineers, researchers, infrastructure teams, product organizations, privacy, security, and Responsible AI teams to build foundational systems that enable AI to be developed, evaluated, and deployed safely and consistently at LinkedIn.

Responsibilities

• Own the technical strategy and architecture for LinkedIn’s large-scale AI Governance, model validation, and policy enforcement infrastructure.

• Design and build high-scale, metadata-driven distributed systems that ingest, process, store, and analyze model lifecycle metadata, evaluation results, production signals, and model outputs.

• Build scalable validation and automated gating systems that support continuous model training and high-velocity deployment while determining whether models meet production standards.

• Translate governance requirements around fairness, bias, privacy, security, safety, and model quality into automated, enforceable platform policies.

• Develop evaluation and analysis frameworks that assess model behavior and generate signals used in governance workflows and production release decisions.

• Build platforms, APIs, SDKs, and abstractions that integrate governance, validation, lineage, and lifecycle tracking into ML development and deployment workflows.

• Partner with ML, AI research, infrastructure, Responsible AI, privacy, security, and product teams to define technical standards and scalable governance solutions.

• Provide Sr. Staff-level technical leadership by influencing architecture across teams, improving developer productivity, mentoring engineers, and raising the technical bar for AI infrastructure.

Qualifications

Basic Qualifications

• BS/BA in Computer Science or related technical field, or equivalent technical experience.

• 5+ years of industry experience in software design, development, and algorithm-related solutions.

• 5+ years of experience programming in languages such as Python, C++, Java, Go, Rust, or Scala.

• 2+ years of experience as an architect, technical lead, or in another technical leadership position.

• 5+ years of experience building large-scale infrastructure, data platforms, machine learning systems, or distributed systems.

• Hands-on experience designing and