Sr. Staff Software Engineer, Systems Infrastructure

Company DescriptionLinkedIn 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 DescriptionThis role will be based in Mountain View, CA, or New York City, CA.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.LinkedIn’s HALO team is building the Evaluation Operating System (EOS), a foundational platform that defines how all AI agents and GenAI products at LinkedIn are measured, evaluated, and continuously improved in production. This is a brand-new, industry-defining problem space with no established playbook, focused on evaluating multi-step, non-deterministic, and personalized AI systems where traditional metrics and testing approaches fall short.EOS acts as the central intelligence layer for AI quality, combining large-scale data pipelines, evaluator models (e.g., LLM-as-judge, reward models), and real-time production monitoring to understand how AI systems behave, where they fail, and how to improve them. The platform includes capabilities like synthetic data generation, adversarial testing, golden dataset management, and live “agent arena” experimentation frameworks (champion/challenger testing) to measure performance across multiple dimensions of quality.As a Senior Staff Engineer, you will own the end-to-end technical vision, architecture, and execution of this platform. This includes designing the data infrastructure for capturing and labeling interactions, building systems to train and deploy evaluation models, and creating real-time monitoring and feedback loops that detect regressions, model drift, and quality degradation in production. You’ll work closely with AI product teams, ML engineers, and infrastructure partners to embed evaluation deeply into the development lifecycle, making it possible for teams across LinkedIn to ship high-quality AI systems with confidence.This role sits at the intersection of distributed systems, data platforms, and machine learning, and is ideal for engineers who want to define how AI quality is measured at scale. The impact is company-wide: the systems you build will directly determine the quality ceiling, safety, and trustworthiness of every AI-powered experience at LinkedIn. ResponsibilitiesOwn the technical vision, architecture, and execution of the Evaluation Operating System (EOS), solving complex, open-ended challenges at the intersection of distributed systems, data infrastructure, and machine learning.Design and build large-scale evaluation infrastructure that enables LinkedIn teams to measure, understand, and continuously improve the quality, reliability, safety, and performance of AI agents and GenAI products.Architect scalable data pipelines and platforms for capturing, processing, labeling, and managing large volumes of AI interactions, evaluation data, golden datasets, and synthetic data.Build and evolve evaluation systems powered by LLM-as-judge, reward models, and other automated evaluators to assess AI systems across multiple dimensions of quality and performance.Develop experimentation and testing frameworks, including adversarial testing, champion/challenger experiments, and agent arena capabilities, to identify weaknesses and drive continuous improvement of AI systems.Establish real-time observability, monitoring, and feedback loops that detect regressions, model drift, quality degradation, and unexpected behavior in production AI systems.Partner closely with AI product teams, ML engineers, and infrastructure organizations to integrate evaluation deeply into the AI development lifecycle and establish consistent evaluation standards across LinkedIn.Lead multiple high-impact, cross-functional initiatives, influencing technical strategy and architectural decisions across AI Platforms and the broader engineering organization.Mentor and develop engineers, raise the technical bar, and help shape the engineering culture and practices of a growing AI platform organization.QualificationsBasic Qualifications:Experience building or evaluating AI Agents in ProductionBS/BA in Computer Science or related technical field or equivalent technical experience5+ years of industry experience in software design, development, and algorithm related solutions5+ years of experience programming in object-oriented languages such as Python, C++, Java, Go, Rust, Scala2+ years of experience as an architect, or technical leadership position5+ years of experience in the industry with leading / building deep learning systemsHands-on experience developing distributed systems or other large-scale systemsPreferred Qualifications:MS or PhD in Computer Science or related technical discipline.10+ years of experience in software design, development, and algorithm related solutions with at least 5 years of experience in a technical leadership position10+ years of experience in an object-oriented programming language such as Python, C++, Java, Go, Rust, Scala5+ years of experience with large-scale distributed systems and client-server architecturesExperience building ML applications, LLM serving, GPU serving.Co-author or maintainer of any open-source projectsExpertise in machine learning infrastructure, including technologies like MLFlow, Kubeflow and large scale distributed systemsExpertise in deep learning frameworks and tensor libraries like PyTorch, Tensorflow, JAX/FLAXSuggested Skills:ML Algorithm DevelopmentMachine Learning and Deep LearningInformation retrieval / recommendation systemsTechnical leadershipLinkedIn 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 InformationEqual Opportunity Statement We seek candidates with a wide range of perspectives and backgrounds and we are proud to be an equal opportunity employer. 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However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by LinkedIn, or (c) consistent with LinkedIn's legal duty to furnish information.San Francisco Fair Chance Ordinance ​Pursuant to the San Francisco Fair Chance Ordinance, LinkedIn will consider for employment qualified applicants with arrest and conviction records.Pay Transparency Policy Statement ​As a federal contractor, LinkedIn follows the Pay Transparency and non-discrimination provisions described at this link: Data Privacy Notice and Compliance Posters for Job Candidates Please use this link to access documents that provide information about how LinkedIn handles the personal data of employees and job applicants, as well as the E-Verify Participation Notice and the Department of Justice Immigrant and Employee Rights Section Right to Work posters: Full-timeFunction: EngineeringExperience level: Mid-Senior LevelIndustry: Internet