Staff Software Engineer, Systems Infrastructure Agent Evaluation

This role will be based in Mountain View 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. 

LinkedIns Core AI is building the Evaluation Operating System (EOS) a foundational Agent Evaluation 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-a-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 recursive Self Improving Agents and live agent arena experimentation frameworks (champion/challenger testing) to measure performance across multiple dimensions of quality. This platform also is responsible for tracing infrastructure for all LinkedIn AI Agents.

As a 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. Youll 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.

Responsibilities

• Own 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.

• Work on reliable and scalable Tracing Infrastructure for LinkedIn AI Agents along with trace debuggability features.

• 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.

• Build and Platformitize Recursive Self Improving Agents

Qualifications :

Basic Qualifications:

• Bachelors Degree in Computer Science or related technical discipline or equivalent practical experience

• 4 years of experience in the industry with leading/ building deep learning systems.

• 4 years of experience with  Java C Python Go Rust C# and/or Functional languages such as Scala or other relevant coding languages

• Hands-on experience developing distributed systems or other large-scale systems.

• Hands-on experience building or evaluating AI Agents in Production.

Preferred Qualifications:

• BS and 8 years of relevant work experienceMS and 7 years of relevant work experience or PhD and 4 years of relevant work experience

• Previous experience working with geographically distributed co-workers.

• Outstanding interpersonal communication skills (including listening speaking and writing) and ability to work well in a diverse team-focused environment with other SRE/SWE Engineers Project Managers etc.

• Experience building ML applications LLM serving GPU serving.

• Experience with search systems or similar large-scale distributed systems

• Expertise in machine learning infrastructure including technologies like MLFlow Kubeflow and large scale distributed systems

• Experience with distributed data processing engines like Flink Beam Spark etc. feature engineering 

• Co-author or maintainer of any open-source projects

• Familiarity with containers and container orchestration systems

• Expertise in deep learning frameworks and tensor libraries like PyTorch Tensorflow JAX/FLAX

Suggested Skills:

• Distributed systems and Backend Systems Infrastructure

• Java/Golang/Rust/Python

• ML Algorithm Development Machine Learning and Deep Learning

• Information Retrieval Recommendation Systems Distributed Serving and Big Data

• Communication and Stakeholder Management

You will Benefit from our Culture

We strongly believe in the well-being of our employees and their families. That is why we offer generous health and wellness programs and time away for employees of all levels. LinkedIn is committed to fair and equitable compensation practices.

The pay range for this role is $175000 - $287000. 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 Information :

Equal Opportunity Statement 

We seek candidates with a wide range of perspectives and backgrounds and we are proud to be an equal opportunity employer. LinkedIn considers qualified applicants without regard to race color religion creed gender national origin age disability veteran status marital status pregnancy sex gender expression or identity sexual orientation citizenship or any other legally protected class.

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No

Employment Type :

Full-time