Top AI Engineer Jobs

Design and deploy production-grade LLM-powered systems (RAG, agents, pipelines). Integrate AI across products, fine-tune and evaluate models, define prompt/modeling best practices, monitor model performance, and mentor engineers.

Perform hands-on data annotation and quality control for classification tasks, script repetitive processes with Python and SQL, build QC checks and dashboards, measure inter-annotator agreement, and document annotation/QC processes while collaborating with data scientists and ML engineers. Hybrid role in NYC requiring process improvement and automation to scale labeling workflows.

Lead end-to-end design, build, and deployment of production LLM/AI systems that convert unstructured life-sciences data into ontologies and actionable commercial insights. Collaborate with founders and engineering to define product direction, system architecture, and scalable infrastructure, then iterate to improve performance, reliability, and user experience.

Design and implement AI-driven website features and integrate AI with NetSuite ERP. Build ML models, data pipelines, chatbots, recommendation and search systems, monitor model performance, ensure data security/HIPAA compliance, and document architectures while collaborating with cross-functional teams.

Build and productionize large-scale data processing, featurization, and inference pipelines for multimodal and vision models. Improve inference frameworks for reliability and cost-efficiency, drive scalability across compute, storage, and queries, and develop evaluation methods to support rapid experimentation and production deployment.

Design and build AI-native, high-performance distributed backend platforms for IT/OT/cybersecurity. Integrate LLMs and ML into production workflows, develop data pipelines and real-time processing, mentor engineers, and drive architectural direction and AI-assisted engineering practices.

Design and implement scalable ML infrastructure solutions, mentor junior engineers, own technical projects, and collaborate across teams to solve complex AI/ML problems.

Design and build end-to-end software products for autonomous vehicles, leading projects in full-stack development, cloud infrastructure, and machine learning pipelines while mentoring junior engineers.

The Principal AI/ML Engineer will lead the AV ML Infra team at GM, focusing on developing scalable ML infrastructure and guiding cross-functional collaboration while driving technical projects from inception to completion.

As a Staff AI/ML Full-Stack Engineer at GM, you'll design and develop end-to-end software products, own technical projects, and mentor junior engineers while focusing on cloud infrastructure and scalable applications.

As a Distinguished Engineer, you will lead technical innovation in AI-driven marketing systems, mentor talent, and guide architecture design. You will collaborate across teams to optimize banking solutions and influence future technology at Capital One.

The Staff AI/ML Engineer will design and implement scalable ML infrastructure solutions, lead projects, mentor peers, and enhance ML productivity at GM.

Work with enterprise customers to identify problems, design and ship LLM-powered and agentic systems to production. Build context-learning capabilities, knowledge-management agents, evaluation and confidence systems, and scalable AI orchestration to drive adoption and expand impact.

In this role, you will lead the AI engineering lifecycle for Snowflake Database Engineering products, enhancing workflows and collaborating to address customer needs.

Lead design and implementation of production agentic AI systems that integrate LLMs with Teak's platform APIs. Build RAG pipelines, MCP/tool integrations, evaluation frameworks, and AI infrastructure on cloud (AWS/GCP/Azure). Contribute backend services in Python, ensure compliance and safety, and operate systems in production including observability and on-call duties.

The AI Engineer will design and implement scalable AI systems, collaborate with stakeholders, and influence AI strategy to integrate AI solutions into business operations while ensuring compliance with regulations.

Build and ship end-to-end AI-first product features: extend a Neo4j Brand Knowledge Graph, design predictive optimization and measurement systems, implement LLM orchestration and integrations, develop React/TypeScript frontends and scalable backends, participate in client-facing demos, and own QA and documentation.

Design and ship end-to-end customer-facing AI experiences, focusing on data-dense, BI-quality frontends (React/TypeScript) while owning APIs and backend design. Build interactive, real-time, agent-driven workflows, manage complex client-side state, and collaborate across the stack to deliver polished production-grade financial analytics and book-close tools.

As a Staff Backend AI Engineer, you will design and operate backend AI systems for Arbiter's care orchestration platform, ensuring reliability, low latency, and scalability while collaborating across teams and mentoring junior engineers.

Lead technical design and development of production AI systems combining multiple models. Build prototypes, production code, reusable libraries, oversee architecture, integrations, QA, and client-facing project leadership.

Design and build autonomous AI agents that navigate websites and execute complex workflows using browser automation and LLMs. Integrate agents with APIs, SaaS, and internal systems, improve reliability through testing and guardrails, optimize performance and costs, and collaborate with product and engineering teams.

Join a team to develop AI tools for financial markets, focusing on building workflows and integrations, improving processes, and ensuring compliance.

Senior AI Engineer to design, develop, and productionize machine learning and AI systems for proprietary trading. Collaborate with researchers and traders to implement, optimize, deploy, and monitor models and scalable infrastructure, and contribute to technical direction and mentoring.

The AI Engineer will prototype AI solutions, advise on integration, manage AI initiatives, and collaborate across technology teams, ensuring alignment with business needs. Roles include monitoring AI trends, engaging stakeholders, and conducting knowledge-sharing sessions on best practices.

Architect, build, and deploy enterprise-grade full stack applications with React front ends and scalable back ends. Use AI-assisted tools and agentic workflows to rapidly prototype and iterate, ensure secure public-facing production readiness, implement authentication/authorization and cloud-based architectures, and partner with stakeholders to evolve prototypes into production systems.