Senior Staff Software Engineer, AI Infrastructure

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 issuesincluding model drift training-serving skew degradation in data quality and shifts in score distributionsensuring that our AI systems remain reliable trustworthy and performant at scale.

As a Sr. Staff Software Engineer you will help define and build LinkedIns 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.

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 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 $198000 to $326000. 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 :

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No

Employment Type :

Full-time