Top AI & Machine Learning Jobs

Lead full-stack development for an AI-native insurance product, owning features from concept to production. Build frontend and backend systems, integrate and orchestrate LLMs and ML models, improve internal developer tooling and CI/CD, collaborate with brokers and customer teams to iterate on AI-driven workflows, and scale the platform across carriers and lines of business.

Opportunistic role for exceptional candidates to define their own mandate applying frontier AI to biomedical science. Work spans agent platform, science, infrastructure, product, and commercial tasks with end-to-end ownership. Must be highly autonomous, AI-native, collaborative, and able to work onsite in South San Francisco.

As an ML Infra Engineer, you'll build and maintain training and inference frameworks, scale distributed workloads, and improve system observability. You'll apply state-of-the-art methods while collaborating with ML and Platform teams.

Lead architecture and implementation of Auger’s data lake and AI platform, building scalable data ingestion, semantic intelligence (knowledge graphs, embeddings, RAG), real-time streaming, and MLOps. Drive system reliability, operational rigor, API-first designs, and mentor engineers while setting platform-wide architectural direction.

The role involves designing and operating an AIOps platform, ensuring reliability, scalability, and efficient automation for AI solutions with collaboration from data teams.

The role involves leading RPA projects, implementing OpenSpan solutions, managing teams, and defining the technical vision and roadmap.

Build and enhance ML systems for custom model training, oversee projects from data intake to model delivery, ensure model quality at scale, create evaluation frameworks, and collaborate with engineers.

Design, build, and maintain MLOps pipelines and infrastructure for Traditional, Generative, and Agentic AI. Implement CI/CD, containerized deployments (OpenShift/Docker/Kubernetes), cloud integration (AWS), automation (Bash/Python), logging/observability, security controls, and collaborate with data scientists and DevOps to deploy and optimize production AI systems.

As an Applied AI Engineer, you'll build AI workflows for SEO, design LLM systems, and optimize operational quality, collaborating closely with a team.

Design, build, deploy, and maintain production AI solutions and agents using Python or .NET on Azure. Implement RAG pipelines, semantic search and vector DBs, work with large-scale data (Databricks/SQL), evaluate model performance, and collaborate with engineers and analysts to productionize experiments.