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Engineering Manager - Machine Learning Infrastructure Jobs

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Engineering Manager, Event Streaming
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Canada , Toronto
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232000.00 - 295000.00 CAD / Year
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Sentry
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Engineering Manager, Event Streaming
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United States , San Francisco
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220000.00 - 280000.00 USD / Year
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Sentry
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Senior Engineering Manager - OHL
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United Kingdom , UK wide (Flexible on location)
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Morgan Sindall Plc
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Engineering Manager (Overhead Line)
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United Kingdom , Glasgow
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Morgan Sindall Plc
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Cloud & DevOps Infrastructure Architect
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India , Pune
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Barclays
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Senior Software Engineer – DevOps Platform
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United States , Palo Alto; New York City
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172000.00 - 228000.00 USD / Year
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Wealthfront
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Senior Developer Success Engineer
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India , Bangalore
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Temporal
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Senior Developer Success Engineer
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Australia , Sydney or Melbourne
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165000.00 - 200000.00 AUD / Year
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Senior Developer Success Engineer
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United States
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145000.00 - 185000.00 USD / Year
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Civil Construction Engineering Internship
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United States , Kansas City; Lenexa; Manhattan; Topeka; Wichita
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RimePro Inc
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Civil Construction Engineering Internship
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United States , Bellevue; Grand Island; Lincoln; Omaha
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RimePro Inc
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Lead Cloud Engineer
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India , Bangalore
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Rearc
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Senior IT Engineer
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India , Hyderabad
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Matillion
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Transportation Engineer
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United States , Quincy
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Klingner & Associates, PC
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Red Hat OpenShift Engineer
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Portugal , Porto
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IVY Partners
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Environmental Discipline Lead
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United Kingdom , Milton Keynes
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EKFB
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Mechanical Lead
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Marcoussis, Milan, Madrid
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DATA4 Group
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Account Manager, Global System Integrator
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United States , New Jersey or Texas
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194500.00 - 456500.00 USD / Year
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Hewlett Packard Enterprise
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Federal Observability Engineer
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United States , HILL AFB
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105500.00 - 243000.00 USD / Year
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Hewlett Packard Enterprise
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Engineering Lead Analyst
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India , Pune
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Citi
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Are you a technical leader passionate about building the robust platforms that power the future of AI? Explore Engineering Manager - Machine Learning Infrastructure jobs, a pivotal role at the intersection of software engineering, distributed systems, and machine learning. Professionals in this field lead teams responsible for designing, constructing, and maintaining the foundational platforms that enable data scientists and ML engineers to develop, train, deploy, and monitor machine learning models at scale. An Engineering Manager for ML Infrastructure typically oversees a team of skilled software and systems engineers. Their core mission is to create reliable, scalable, and efficient infrastructure that abstracts away complexity, allowing product teams to focus on model innovation rather than underlying systems. Common responsibilities include architecting large-scale data processing pipelines, developing model training and serving frameworks, implementing robust monitoring and observability tooling, and ensuring high availability and cost-efficiency of ML systems. They are also deeply involved in strategic planning, setting technical direction, and managing the full software development lifecycle for critical platform services. To excel in these jobs, individuals usually possess a strong blend of technical depth and leadership acumen. Typical requirements include a proven background in software engineering with expertise in distributed systems, cloud computing (AWS, GCP, Azure), and containerization technologies like Kubernetes. A solid understanding of machine learning workflows and frameworks (e.g., TensorFlow, PyTorch) is essential to build empathetic and effective platforms. Leadership skills are paramount; successful managers excel at mentoring engineers, driving cross-functional collaboration with data science and product teams, managing project roadmaps, and fostering a culture of operational excellence and innovation. They are strategic thinkers who can translate business needs into technical vision and execution. For those seeking leadership roles that directly enable cutting-edge AI advancements, Engineering Manager - Machine Learning Infrastructure jobs offer a challenging and rewarding career path. It is a profession dedicated to building the invisible engine of the AI revolution, requiring a unique synthesis of technical expertise, architectural vision, and people leadership to empower organizations to leverage machine learning effectively and reliably.

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