Senior Manager, Software Engineering (Distributed Systems)

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

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We are building advanced machine learningdriven 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 M

L.
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 tea

ms.
Responsibili

• tiesDefine and drive the vision for ML-powered infrastructure observability with a focus on Availability, Reliability, Detection Accuracy and Operational Excelle
• nce.Architect and scale analytical modeling systems
• for:Real-time anomaly detection across metrics, logs and tr
• acesSignal correlation and root cause anal
• ysisFailure prediction and risk sco
• ringCapacity forecasting and saturation predic
• tionIntelligent alert noise reduc
• tionBuild ML pipelines that operate at hyperscale across distributed systems in Azure and other public cloud environme
• nts.Lead the development of statistical, time-series and deep learning models tailored for infrastructure telemetry d
• ata.Integrate ML models directly into monitoring and incident management workfl
• ows.Ensure models are production-grade: reliable, explainable, scalable and cost-effici
• ent.Drive execution in partnership with infrastructure engineering, product and architecture te
• ams.Establish strong service ownership practices including SLOs, SLAs and operational metrics for ML-powered servi
• ces.Build and mentor a high-caliber team of ML engineers and distributed systems engine
• ers.Promote rigorous experimentation, model evaluation frameworks and data-driven decision mak
• ing.Recruit top talent in ML systems and infrastructure engineer

ing.
Required Skills / Exper

• ience12+ years of experience in software development with 3+ years managing engineering t
• eams.Strong background in Machine Learning applied to large-scale systems or infrastructure prob
• lems.Large-scale data anal
• yticsExperience productionizing ML models in distributed cloud environm
• ents.Strong foundation in Distributed Systems architec
• ture.Experience building or operating observability or telemetry platforms at s
• cale.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 sto
• res).Strong service ownership mindset with experience defining and operating services with SLOs/
• SLAs.Ability to balance research-oriented thinking with practical production deli
• very.Proven track record of recruiting and developing high-performing technical t
• eams.Excellent written and verbal communication sk

ills.
Preferred Qualific

• ationsExperience building ML-driven monitoring or availability plat
• forms.Background in infrastructure reliability engineering or SRE environ
• ments.Experience designing low-latency ML inference sy
• stems.Contributions to research, patents, or technical publications in applied ML or distributed sy