ray jobs in San Diego

Build and improve machine learning systems for real-time transaction underwriting decisions at Affirm. Develop classification models using gradient-boosted trees and deep learning, construct feature pipelines from proprietary data, prototype and productionize models with appropriate risk controls, and monitor model/data health across batch and real-time systems.…See more Strong Python skills, production-quality code experience, and familiarity with ML lifecycle tooling (Kubeflow, Airflow, MLflow) required; 2+ years as an ML engineer or relevant PhD expected.

Engineer I, Software on the Factory Team will develop and maintain infrastructure tooling, CI/CD pipelines, and developer workflows for autonomous systems simulation and testing. Responsibilities include containerized development environments (Docker, Kubernetes), deployment configurations (Terraform), documentation, and collaboration with autonomy and platform teams.…See more The role requires foundational Python skills, Linux proficiency, and understanding of software engineering practices; experience with test automation, simulation environments, or distributed computing is preferred.

Senior full-stack engineer responsible for building complete features across React frontends, backend services, APIs, and data storage. You will design and evolve APIs, contribute to platform architecture, and deploy containerized services to diverse environments (local, CI/CD, air-gapped Kubernetes).…See more You'll work directly with users and cross-functional teams to translate complex workflows into reliable, intuitive applications, with hands-on ownership of testing, troubleshooting, and iterative improvement across the full stack.

As a Senior Scientist in Machine Learning, you will design, develop, and evaluate state-of-the-art foundation models at scale, including pre-training and fine-tuning large-scale systems with multimodal biological data, natural language, and structured inputs. You will architect hybrid models integrating Large Language Models (LLMs) with Graph Neural Networks (GNNs) for multi-hop reasoning over biological knowledge graphs, and develop Relational Foundation Models (RFMs) for zero-shot predictive tasks.…See more You will lead the design of efficient distributed training strategies across multiple GPU nodes, mentor junior staff, and transition research prototypes into production systems. The role requires a PhD in Computer Science or Machine Learning with 5+ years of experience, strong Python and deep learning library expertise, proven multi-GPU distributed training experience, and a track record of peer-reviewed AI/ML research publications.

Engineer II, Software – Factory will develop and maintain automated simulation, testing, and data-analysis infrastructure for autonomous systems. Responsibilities include building containerized development environments using Docker and Kubernetes, contributing infrastructure-as-code configurations with Terraform, supporting CI/CD pipelines and source control, and creating developer documentation and tooling.…See more The role requires a Master's degree in Computer Science or related field, foundational Python experience, Linux fluency, and familiarity with Git, Docker, and automated testing frameworks.

Staff Engineer role building full-stack applications for the Forge platform, spanning React frontends, backend services in Go or Python, and containerized deployments. Responsibilities include designing and evolving APIs, data models, and platform architecture; deploying and troubleshooting services in Kubernetes and air-gapped environments; and collaborating with users, product, and engineering teams to deliver features end-to-end.…See more Required: production experience across frontend and backend, TypeScript/React proficiency, backend services in Go/Python, database/data modeling, containers/CI-CD, and strong communication. Preferred: hands-on Kubernetes, job orchestration tools (Ray, Argo, Dagster), schema-driven APIs (OpenAPI, gRPC), and experience with multi-environment deployments.

Lead end-to-end AI platform architecture and infrastructure at enterprise scale, designing and implementing generative AI capabilities including LLMs, RAG systems, and agentic AI with a focus on model serving, inference optimization, and production deployment. Establish MLOps/LLMOps best practices, build automated CI/CD pipelines tailored for AI/ML applications, and architect vector database integration and cloud AI/ML services.…See more Mentor engineering teams, drive cross-functional collaboration between software, product, and data teams, and continuously evaluate and optimize AI platform performance, scalability, and reliability.

Staff Engineer who will design, build, and operate the Forge Platform—a distributed-systems foundation serving autonomy, ML Ops, simulation, and application teams. You will own architecture and technical standards for workflow orchestration, asynchronous processing, event-driven systems, and long-running service workflows while remaining hands-on in implementation, production troubleshooting, and reliability improvement.…See more Required: strong production experience with distributed systems, cloud-native platforms, or backend infrastructure; fluency in Go and Python; deep understanding of failure handling, consistency, fault tolerance, and state management; and ability to turn recurring infrastructure needs into reusable platform capabilities. You will work across Kubernetes, service networking, observability, and data pipelines to enable downstream teams to move faster on mission-critical systems.

Staff Engineer responsible for designing, building, and operating the Forge Platform—a distributed-systems foundation serving autonomy, ML, simulation, and test teams. You will architect cloud-native backend infrastructure, establish reusable platform capabilities, define technical standards, and remain hands-on in production troubleshooting, performance analysis, and reliability improvement.…See more Required: deep expertise in production distributed systems, Go and Python, workflow orchestration, and the ability to translate complex architecture into usable interfaces across multiple downstream teams.

Senior Staff Engineer responsible for designing and building an MLOps platform that supports distributed AI training, reinforcement learning, and foundation model development at scale. You will architect Kubernetes-native infrastructure for GPU workloads, design self-service AI development workflows, manage the data and model lifecycle, and lead platform distribution across cloud, on-premises, and air-gapped environments.…See more The role requires deep expertise in modern AI frameworks (PyTorch, Hugging Face Transformers), distributed systems, GPU scheduling, and cloud-native infrastructure, with a focus on enabling researchers and engineers to move from experimentation to production rapidly.

Lead end-to-end AI platform architecture and design for generative AI solutions, including LLMs, RAG systems, and agentic AI. Build MLOps/LLMOps pipelines with automated CI/CD, model serving infrastructure, and vector database integration on enterprise cloud platforms.…See more Establish best practices, mentor engineering teams, and drive seamless AI application integration into production systems while optimizing for scalability, security, and performance.

Lead the design, development, and operation of Qualcomm's enterprise AI platform, supporting agentic AI, model lifecycle management, and multi-cloud ML/inference infrastructure. Own core platform services including identity/RBAC, secrets, service meshes, observability, vector stores, and model gateways across on-prem GPU clusters and managed cloud services.…See more Manage a ~10-engineer global team (platform, SRE, MLOps/LLMOps), drive incident response and continuous improvement, and partner with product and security teams on AI governance and high-impact use cases.

Build and operate the data and ML infrastructure powering an AI platform for materials science, owning both sides: data pipelines that ingest and curate large-scale scientific output into training-ready formats, and model packaging, serving, monitoring, and CI/CD systems that move models safely from research to production across customer environments. You will design data ingestion and transformation workflows, implement validation and quality gates, package and version models with reproducible builds, run models through batch and online inference with safe rollout and rollback, monitor for drift and degradation, and build observability and internal tooling for engineering and science teams.…See more The role requires 6+ years shipping production software with deep expertise in data systems, ML infrastructure, containers, orchestration, and observability.