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 to build the platforms that enable LinkedIn to evaluate, monitor, and continuously improve machine learning models at scale. Our AI systems power recommendations, search, ads, LLMs, computer vision, and other intelligent experiences used across LinkedIn. The Model Evaluation team develops robust, scalable frameworks that empower engineers and researchers to rigorously quantify model quality, conduct comparative analysis against established baselines, proactively identify performance regressions, and seamlessly bridge the gap between offline evaluation metrics and real-world production outcomes. The Model Observability team engineers robust, highly scalable infrastructure that delivers continuous, real-time insights into model performance and behavior in production. We empower teams to proactively detect and diagnose critical issues—including model drift, training-serving skew, degradation in data quality, and shifts in score distributions—ensuring that our AI systems remain reliable, trustworthy, and performant at scale. As a Sr. Staff Software Engineer, you will help define and build LinkedIn’s next generation of Model Evaluation and Observability infrastructure, solving complex distributed systems and ML platform problems while influencing how AI systems are evaluated and understood across the company. Responsibilities: • Own the technical strategy and architecture for large-scale Model Evaluation and Observability infrastructure, developing solutions that span multiple product lines and AI use cases. • Design highly available, distributed architectures to ingest, process, and analyze high-volume telemetry data from a variety of models, encompassing recommendation and ranking, machine learning, LLMs and generative AI systems. • Build scalable model evaluation platforms that enable ML engineers and researchers to measure model quality, compare models, identify regressions, and understand model behavior across experimentation and production environments. • Lead the diagnosis and resolution of complex, cross-team performance bottlenecks, data quality issues, and systemic reliability challenges in the ML lifecycle. • Define and implement "observability-by-default" frameworks that enable ML engineers to iterate faster by seamlessly bridging the gap between experimentation, offline evaluation, and production reliability. • Build capabilities for identifying and diagnosing issues such as model regressions, drift, training-serving skew, score-distribution changes, and data-quality problems. • Improve developer productivity by making it easier for teams to evaluate, monitor, and diagnose production ML systems. • Mentor and influence engineers across the organization, establish strong engineering practices, and raise the technical bar for large-scale ML infrastructure. • Serve as a technical leader across multiple Model Evaluation and Model Observability initiatives, driving architecture and execution across organizational boundaries. • Anticipate future scale and complexity requirements, proactively evolving our architecture to handle increasing data volumes, diverse model types, and evolving compliance/governance standards. 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, machine learning systems, or distributed systems • Hands-on experience designing and developing distributed systems or other large-scale production platforms 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, model lifecycle platforms, or large-scale production ML systems. • Experience building model evaluation, model monitoring, ML observability, experimentation, model validation, or model quality infrastructure. • Experience with generative recommendation architectures, including LLM/SLM-based rankers, semantic ID representations, and evaluation of sequence-to-sequence or autoregressive ranking models. • Experience designing platforms that collect and process model outputs, metrics, metadata, telemetry (OTEL or OpenInferenceTelemetry), or other production ML signals at scale. Suggested Skills: • Model Evaluation • Model Observability / ML Observability • Machine Learning Infrastructure • Production Machine Learning Systems • Large-Scale Distributed Systems • MLOps 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 careers.linkedin.com . 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. LinkedIn is committed to offering an inclusive and accessible experience for all job seekers, including individuals with disabilities. Our goal is to foster an inclusive and accessible workplace where everyone has the opportunity to be successful. If you need a Reasonable Accommodation to search for a job opening, apply for a position, or participate in the interview process, connect with us and describe the specific Accommodation requested for a disability-related limitation. Fill out an Accommodation request here: app.smartsheet.com Reasonable accommodations are modifications or adjustments to the application or hiring process that would enable you to fully participate in that process. Examples of reasonable accommodations include but are not limited to: • Documents in alternate formats or read aloud to you • Having interviews in an accessible location • Being accompanied by a service dog • Having a sign language interpreter present for the interview A request for an accommodation will be responded to within three business days. However, non-disability related requests, such as following up on an application, will not receive a response. LinkedIn will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. 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: lnkd.in. Global 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: linkedin.com.

Requirements

Must-haves

Own the technical strategy and architecture for large-scale Model Evaluation and Observability infrastructure, developing solutions that span multiple product lines and AI use cases.
Lead the diagnosis and resolution of complex, cross-team performance bottlenecks, data quality issues, and systemic reliability challenges in the ML lifecycle.
Define and implement "observability-by-default" frameworks that enable ML engineers to iterate faster by seamlessly bridging the gap between experimentation, offline evaluation, and production reliability.
Build capabilities for identifying and diagnosing issues such as model regressions, drift, training-serving skew, score-distribution changes, and data-quality problems.
Improve developer productivity by making it easier for teams to evaluate, monitor, and diagnose production ML systems.
Mentor and influence engineers across the organization, establish strong engineering practices, and raise the technical bar for large-scale ML infrastructure.
Serve as a technical leader across multiple Model Evaluation and Model Observability initiatives, driving architecture and execution across organizational boundaries.
Anticipate future scale and complexity requirements, proactively evolving our architecture to handle increasing data volumes, diverse model types, and evolving compliance/governance standards.
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, machine learning systems, or distributed systems
Hands-on experience designing and developing distributed systems or other large-scale production platforms
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.
Model Evaluation
Model Observability / ML Observability
Machine Learning Infrastructure

Nice-to-haves

MS or PhD in Computer Science or related technical discipline
Experience building machine learning infrastructure, model lifecycle platforms, or large-scale production ML systems.
Experience building model evaluation, model monitoring, ML observability, experimentation, model validation, or model quality infrastructure.
Experience with generative recommendation architectures, including LLM/SLM-based rankers, semantic ID representations, and evaluation of sequence-to-sequence or autoregressive ranking models.
Experience designing platforms that collect and process model outputs, metrics, metadata, telemetry (OTEL or OpenInferenceTelemetry), or other production ML signals at scale.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningaimlpythonjavagorustllm

Platform: smartrecruiters
Effort: medium

Benefits

Benefits detected in the listing.

Vision