Description

The Salesforce Monitoring Cloud team is looking for an engineering leader with deep expertise in AI/ML and analytical modeling for infrastructure systems to lead high caliber engineering teams building the next generation of intelligent availability monitoring platforms at massive scale.

Monitoring Cloud is a foundational part of Salesforce Infrastructure that ensures the reliability and availability of Salesforce products globally. We own the entire telemetry stack — from lightweight agents that emit metrics, logs, traces and events, to large-scale distributed backend systems that process petabytes of telemetry data in real time across public cloud environments.

We are building advanced machine learning–driven detection and analytical systems that power proactive incident identification, anomaly detection, capacity forecasting, failure prediction and automated remediation across distributed cloud infrastructure. This role focuses on deep applied ML and statistical modeling for infrastructure observability — not generative AI or enterprise application ML.

As an engineering leader, you will drive the architecture and implementation of scalable ML systems embedded directly into our monitoring cloud. You will lead teams building analytical pipelines, detection models, signal correlation engines and intelligent automation systems that improve availability, reduce MTTR and enhance system resilience. You are equally passionate about technical depth, operational excellence, and building high-performing teams.

Responsibilities

• Define and drive the vision for ML-powered infrastructure observability with a focus on Availability, Reliability, Detection Accuracy and Operational Excellence.

• Architect and scale analytical modeling systems for:

  • Real-time anomaly detection across metrics, logs and traces

  • Signal correlation and root cause analysis

  • Failure prediction and risk scoring

  • Capacity forecasting and saturation prediction

  • Intelligent alert noise reduction

• Build ML pipelines that operate at hyperscale across distributed systems in Azure and other public cloud environments.

• Lead the development of statistical, time-series and deep learning models tailored for infrastructure telemetry data.

• Integrate ML models directly into monitoring and incident management workflows.

• Ensure models are production-grade: reliable, explainable, scalable and cost-efficient.

• Drive execution in partnership with infrastructure engineering, product and architecture teams.

• Establish strong service ownership practices including SLOs, SLAs and operational metrics for ML-powered services.

• Build and mentor a high-caliber team of ML engineers and distributed systems engineers.

• Promote rigorous experimentation, model evaluation frameworks and data-driven decision making.

• Recruit top talent in ML systems and infrastructure engineering.

Required Skills / Experience

• 12+ years of experience in software development with 3+ years managing engineering teams.

• Strong background in Machine Learning applied to large-scale systems or infrastructure problems.

• Large-scale data analytics

• Experience productionizing ML models in distributed cloud environments.

• Strong foundation in Distributed Systems architecture.

• Experience building or operating observability or telemetry platforms at scale.

• Experience with public cloud platforms such as Azure, AWS or GCP.

• Experience with large-scale data processing frameworks (e.g., Kafka, Spark, stream processing systems, NoSQL stores).

• Strong service ownership mindset with experience defining and operating services with SLOs/SLAs.

• Ability to balance research-oriented thinking with practical production delivery.

• Proven track record of recruiting and developing high-performing technical teams.

• Excellent written and verbal communication skills.

Preferred Qualifications

• Experience building ML-driven monitoring or availability platforms.

• Background in infrastructure reliability engineering or SRE environments.

• Experience designing low-latency ML inference systems.

• Contributions to research, patents, or technical publications in applied ML or distributed systems.