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Solutions Architect driving AWS cloud adoption for aerospace and satellite customers globally—designing architectures, running PoCs, writing reference architectures, and enabling partners across space domain segments.
The Transit Gateway team builds and scales AWS's central networking hub, focusing on distributed systems and software-defined networking to connect VPCs and on-premises networks. Engineers design and implement highly available, low-latency routing solutions at cloud scale.
The Software Development Engineer will design and build scalable, distributed supply chain management systems to support AWS global infrastructure. The role involves working in an agile environment to develop high-performance backend services and UIs that manage inventory and warehouse operations.
The ideal candidate has experience working with large datasets and distributed computing technologies. They relish working with large volumes of data, enjoy the challenge of highly complex technical contexts, and above…
The Senior Distributed Software Engineer will design and build scalable, reliable distributed storage services optimized for AI/ML workloads using Golang and Kubernetes. This role involves end-to-end ownership of storage-as-a-service features, collaborating across teams to support NVIDIA's critical business infrastructure.
Build and scale backend infrastructure for message delivery, pacing algorithms, and personalization engines at Uber using Python/Java/Go/TypeScript, microservices, SQL/NoSQL, Docker, Kubernetes, and distributed computing frameworks like Spark or Flink.
Software Engineer II building backend systems for message delivery optimization, frequency capping, and real-time orchestration at scale, using Python/Java/Go/TypeScript, microservices, SQL/NoSQL, Docker/Kubernetes, and distributed computing frameworks.
By clicking the “Apply” button, I understand that my employment application process with Takeda will commence and that the information I provide in my application will be processed in line with Takeda’s Privacy Notice…
Overview Overview Microsoft AI is looking for a Software Engineer AI Infra to help build the SOTA LLM models. We’re looking for someone who will bring an abundance of positive energy, empathy, and kindness to the team…
Develops and scales automated frameworks for Android XR app compatibility, integrating AI/ML (LLMs, computer vision) to detect UI/rendering issues across physical devices, emulators, and cloud environments. Works with Android XR teams and third-party developers to improve XR app experiences.
Design and scale large-scale, data-intensive backend systems and REST APIs, mentor engineers, and improve cloud infrastructure using TypeScript, Node.js, Python, C#, and AWS.
Software developer on the Developer Connect Insights (SDLC Graph) team, building a service that correlates SDLC events with application runtimes using Java, C++, Python, or Go on large-scale cloud infrastructure.
Designs, builds, and optimizes scalable data pipelines and infrastructure to integrate, process, and manage data for cloud connectivity solutions, ensuring reliability and performance in a global SaaS environment.
Data Engineer designing and building data ingestion pipelines and ETL processes on-site in London for national security clients, using Python, Apache Spark, NiFi, Kafka, AWS, and Palantir Foundry.
Develops and maintains AI/ML models for Google’s Search Ads auto-bidding system, optimizing bid predictions to improve advertiser ROI and satisfaction using deep learning and large-scale systems.
Software engineer on Google's Maps/Geo team implementing GenAI solutions using LLMs and ML infrastructure, with work on spatial data structures and computational geometry algorithms.
Principal ML Engineer builds and deploys scalable AI systems for industrial operations, transforming unstructured data into actionable insights using deep learning, generative AI, and computer vision.
Design and build scalable data pipelines for industrial AI platforms, integrating oil & gas and manufacturing data using Python, SQL, and cloud services to enable smarter operations.
Build and scale the distributed GPU training infrastructure and MLOps platform that powers Atoms’ autonomous transport models, using Kubernetes, Ray, and MLflow.
Build and scale distributed GPU training infrastructure for autonomous transport models using Kubernetes, MLOps tools, and high-throughput data pipelines.
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