datadog jobs in San Diego

Build production AI features and services using foundation model APIs, retrieval-augmented generation (RAG), and agentic workflows. You will implement scalable microservices in Python or Java, develop evaluation and quality frameworks for AI systems, and collaborate across teams to integrate AI capabilities into enterprise workflows.…See more The role requires hands-on applied AI experience, strong backend or platform engineering skills, and familiarity with vector databases, LLM orchestration tools, and cloud infrastructure.

Qualcomm IT seeks a Kubernetes generalist to construct, deploy, manage, and troubleshoot high-performance bare-metal on-premises and cloud-based multi-tenant Kubernetes clusters using Rancher (RKE2, GKE, EKS, AKS). Day-to-day responsibilities include monitoring cluster performance and availability, developing platform automation scripts, and leading projects such as self-healing system implementation, Kubernetes version validation, multi-tenancy pattern integration (vcluster), and policy migration (Gatekeeper OPA to Validating Admission Policy).…See more Required skills include 2+ years Kubernetes administration, advanced proficiency in Kustomize/Helm, Docker/ContainerD, git/GitHub, intermediate Kubernetes networking (Cilium) and control-plane architecture knowledge, plus intermediate Python/Go and bash scripting. The role requires full-time onsite work in San Diego, CA.

As a SW Test Development Engineer 2, you will design and build automation tools and frameworks to test Dexcom's continuous glucose monitoring devices and mobile apps (iOS/Android). You will develop in Python or Java to create testing APIs, integrate tools like Jira, Testrail, Slack, and GitHub, and manage end-to-end test suites in CI/CD pipelines.…See more The role requires 2–3 years of software development experience with object-oriented languages, hands-on familiarity with cloud platforms (AWS/GCP), containerization (Docker), and embedded/mobile device automation.

Own and deliver small to medium machine learning system components from design through production deployment, including building data pipelines, training and evaluating models, and implementing MLOps monitoring. Write high-quality Python code to translate technical requirements into maintainable solutions, working with frameworks like scikit-learn, TensorFlow, PyTorch, and HuggingFace.…See more Design and deploy ML models as microservices, APIs, batch jobs, or streaming components on AWS, with responsibility for model performance metrics, data drift detection, and retraining triggers. Collaborate across Data Engineers, Software Engineers, Data Scientists, and product stakeholders to deliver project objectives.