Top AI Engineer Jobs

Develop and optimize AI models and training systems for robotics applications, focusing on design, implementation, and performance optimization of ML models.

The job involves building responsive web dashboards, advanced data visualizations, and analytics tools for unmanned aircraft systems, requiring a deep understanding of UI/UX design and data presentation.

As an AI Product Engineer, you will develop and deploy LLM-powered language learning products, enhance existing features, and collaborate cross-functionally to optimize performance and user experience.

Lead end-to-end AI-first transformation for operations: define problems, design architecture, build agentic systems and automation pipelines, integrate with CRMs and operational tooling, ship production solutions and drive company-wide AI adoption.

As a Research Engineer, you will optimize AI performance, focusing on kernel development for large-scale ML workloads, profiling bottlenecks, and collaborating with teams to enhance model training and inference.

As a Staff ML Engineer, you'll lead complex model quality improvements in clinical AI, spanning research and engineering, optimizing performance under high compliance conditions.

Integrate LLMs, RAG, and agentic-AI into secure decision-support software. Design prompts and inference workflows, connect AI to backend services, evaluate outputs, build tests, document designs, and collaborate with engineering, security, and DevSecOps teams to deliver reliable mission-capable AI features.

The Staff ML/AI Platform Engineer will design, prototype and deploy AI/ML models, optimize pipelines, mentor junior engineers, and collaborate closely with other teams to develop machine learning solutions.

The Principal Software Engineer will lead the design and build-out of an AI Context Layer, ensuring high-quality data for AI systems, while managing access control and developer interfaces, and leading cross-team projects.

Design and build secure, cloud-native AI applications using LLMs, RAG, and Amazon Bedrock to provide AI-assisted search, summarization, and clinical question-answering; integrate with enterprise healthcare systems using FHIR/HL7, implement vector search and prompt orchestration, ensure Fed security/privacy compliance and CI/CD, and support testing, monitoring, and governance for production readiness.

The Senior Enterprise Data Architect will design and govern enterprise data and AI architecture by guiding implementation patterns, ensuring scalable solutions, and building prototypes for validation. The role requires deep cloud data platform expertise and strong stakeholder management skills.

As a Software Engineer in Frontier AI Infrastructure, you'll design secure systems for the public sector, improve engineering standards, and collaborate with cross-functional teams to meet government requirements. You'll also participate in customer engagements and contribute to the product strategy while ensuring compliance with federal regulations.

Develop and implement AI models and algorithms using Azure technologies, focusing on full-stack development, AI solutions, and collaboration with stakeholders to optimize business processes.

Lead the design and optimization of batch and streaming data pipelines, ensuring high-quality data delivery for AI and analytics, while mentoring and promoting engineering best practices.

Build and ship end-to-end AI-native product features, prototype with modern AI models/APIs, collaborate with product/design, maintain fast reliable systems, and improve dev workflows and deployments.

Lead technical discovery and solution design for enterprise MedTech customers, translate claims data into commercial insights, deliver data via secure sharing/APIs/warehouses, own RFPs and security reviews (SOC2/HIPAA/BAA), enable demo environments and AI narratives, and institutionalize best practices across Sales, Product, Data, and Engineering.

Design, build, and ship production AI agents and ML systems for retail merchandising, document understanding, retrieval/ranking, and evaluation. Own full model lifecycle from research to production, build evaluation/observability, and collaborate with engineers, PMs, and store owners to drive measurable merchant impact.

Lead technical architecture and delivery of end-to-end AI solutions across the Workday ecosystem. Design data flows, integrations, security and RAG/LLM patterns; advise executive stakeholders; lead technical workstreams; collaborate with Product and Engineering; ensure privacy, ethical AI, and scalable deployments for complex global customers.

Lead developer advocacy for AI and agentic security: advise security companies, accelerate NVIDIA stack adoption, guide partner integrations, map ecosystems, collaborate cross-functionally, run community events, and feed product teams with field insights.

Design, develop, and deploy autonomous AI agents and ML models to automate cybersecurity use cases (incident investigation, vulnerability prioritization). Integrate LLMs, build data pipelines with vector databases, implement MLOps and cloud AI deployments, enforce safety/compliance guardrails, and optimize workflows for automated remediation.

Lead the security architecture and implementation for an enterprise AI platform: secure cloud infrastructure, data protection, IAM, tenant isolation, and GenAI workflows. Embed security into CI/CD, IaC, containers, secrets management, and operations. Design secure customer deployment patterns, support enterprise security reviews, and partner on compliance (SOC 2, ISO 27001, SSPA) while personally implementing critical security capabilities and guiding cross-functional engineering teams.

Design, build, and maintain scalable enterprise full-stack applications using Java, Spring Boot, microservices, and modern front-end frameworks. Implement cloud-based solutions on AWS, contribute to CI/CD and DevOps automation, optimize performance and security, and integrate AI-driven capabilities using Python and orchestration tools like LangChain and LangGraph.

Build and deploy machine-learning prototypes and tools for AI security; analyze large cybersecurity datasets; support AI red teaming and adversarial ML; collaborate with researchers to translate research into operational capabilities.

Design and implement ML-based prototypes, tools, and systems for AI security. Collaborate with researchers to execute experiments, analyze large cybersecurity datasets, support adversarial ML and red teaming, and translate research into deployable, maintainable software solutions.

You will build agentic AI products across web, backend, and iOS, owning features end-to-end and mentoring junior teammates.