60 Linkedin Infrastructure Software Engineer Jobs Hiring Near You

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.

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.

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 developing distributed systems or other large-scale production platforms.

• Experience designing or working with metadata systems, data platforms, large-scale storage systems, or data-processing pipelines used to capture and reason over complex system or ML lifecycle state.

• Experience working with machine learning systems or production ML lifecycle workflows such as training, evaluation, validation, inference, or deployment.

Preferred Qualifications

• MS or PhD in Computer Science or related technical discipline.

• 10+ years of experience in software design and development, including significant experience in technical leadership positions.

• 5+ years of experience designing and building large-scale distributed systems and production infrastructure.

• Experience building machine learning infrastructure, MLOps platforms, model lifecycle systems, or production ML platforms.

• Experience building AI governance, model validation, model compliance, Responsible AI, trust and safety, or policy enforcement systems.

• Experience designing metadata platforms, data models, data pipelines, feature stores, model registries, lineage systems, or other platforms that track ML lifecycle information.

• Experience building systems that evaluate model behavior or enforce requirements related to fairness, bias, privacy, safety, security, or model quality.

• Experience building automated validation, policy decisioning, deployment gating, or compliance enforcement systems.

• Experience building developer-facing APIs, SDKs, frameworks, or platform abstractions used across multiple engineering teams.

• Demonstrated ability to define technical strategy and influence architecture across organizational and team boundaries.

Suggested Skills

• AI Governance / Responsible AI

• Machine Learning Infrastructure / MLOps

• Model Lifecycle & Model Validation

• Metadata Platforms

• Policy Enforcement / Automated Guardrails

• Model Evaluation

• Fairness / Bias / Privacy

• Large-Scale Distributed Systems

• Data Platforms / Data Pipelines

• Production Machine Learning Systems

• Technical Leadership

LinkedIn is committed to fair and equitable compensation practices.

The pay range for this role is $198,000 to $326,000. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to skill set, depth of experience, certifications, and specific work location. This may be different in other locations due to differences in the cost of labor.

The total compensation package for this position may also include annual performance bonus, stock, benefits and/or other applicable incentive compensation plans. For more information, visit https://careers.linkedin.com/benefits.

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