Top AI Engineer Jobs

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.

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.

Lead design, deployment, and monitoring of LLM-based AI solutions. Build end-to-end generative AI applications, fine-tune models, integrate LLM APIs into products, ensure post-deployment performance, and collaborate cross-functionally within an Agile environment to drive measurable business impact in healthcare.

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.

Design, build, and ship AI-powered observability features using LLMs and agentic workflows. Rapidly prototype, iterate with real users, integrate AI into alerting, runbooks, and developer workflows, and ensure solutions are scalable, maintainable, and production-ready while collaborating cross-functionally.

Own and build production-grade integrations between Brex and external AI assistant ecosystems (OpenAI, Claude, Slack, etc.). Design secure, auditable patterns for data access and permissions, operate resilient integrations, define UX/reliability standards, and evangelize reusable AI capabilities across the platform. Partner cross-functionally to ensure safety, trust, and customer reliance.

Build the product harness for an Audit Agent: design backend systems, data contracts, APIs, and feedback/evaluation loops; ship customer-facing features; run experiments; and collaborate with frontend, agent, and customers to improve workflows and trust.

Own and build production-grade integrations between Brex and external AI assistant ecosystems (OpenAI, Claude, Slack, etc.). Define secure, auditable patterns for data/actions, permissions, and UX standards. Ship resilient integrations, partner cross-functionally, and create reusable AI capabilities and standards for external and internal use.

Design, develop, and maintain scalable CPQ solutions on Salesforce. Implement Apex and Lightning components, optimize existing systems, and mentor junior developers.

Design and deliver scalable full-stack AI systems: architect backend AI agents and frontend React apps, optimize RAG/indexing and Lambda workflows, integrate frontend/backend, improve performance and CI/CD, test and red-team agents, analyze with DataDog/Databricks, mentor junior engineers and set development standards.

Lead design, build, and deploy production-grade Voice AI and LLM capabilities (ASR, real-time transcription, bilingual switching). Set architecture and enterprise standards, mentor engineers, ensure HIPAA-compliant, secure, and reusable solutions, and align roadmaps with business partners and vendors.

Design, build, and deploy production-grade ML and AI solutions for behavioral health. Architect end-to-end MLOps pipelines, optimize and monitor models in production, evaluate emerging AI trends, collaborate cross-functionally, and provide technical leadership and mentorship to engineering teams while ensuring responsible, scalable, and secure AI deployments.

Lead cloud migrations for voice contact center solutions, design AI/ML capabilities, improve automation and governance, and ensure production readiness while collaborating with cross-functional teams.

Lead design, development, and production deployment of AI/ML solutions and conversational chatbot platforms. Own end-to-end implementation, MLOps/CI-CD, containerized deployments, solution architecture, security and governance, mentor engineers, and collaborate with product, enterprise architecture, and vendors to deliver scalable, monitored AI systems.

Lead technical design and delivery of production AI/ML and GenAI solutions (RAG, LLMs, agents). Build, tune, deploy, and monitor models and APIs, own MLOps practices, operate cloud AI workloads, ensure software quality and responsible AI, and partner with cross-functional stakeholders while mentoring engineers.

Own and improve the AI Social render pipeline and agent loops that generate on-brand Instagram carousels and reels. Build prompts, structured outputs, tool-calling, and media composition to ship high-quality generative image, video, voice, and music products in collaboration with product, design, and content strategy.

As a Senior Embodied AI Engineer, you will design and deploy intelligent control systems, manage simulations, and apply machine learning methodologies to enhance robotic performance in manufacturing environments.

The Sr AI Engineer designs and develops AI solutions to optimize business operations, integrating machine learning capabilities to enhance decision making and automation across functions such as supply chain and finance.

Lead design, build, and operation of production AI systems and inference stacks (vLLM) across AI Factory platforms. Implement RAG, MLOps, storage/networking, Kubernetes/containers, monitoring, security, and on-call incident response. Mentor engineers, engage clients, author runbooks, and contribute reusable patterns and tooling.

Responsible for designing and delivering multi-agent systems for AI products, collaborating with data scientists and offshore teams, and writing production-grade Python code.

The AI Engineer will develop AI tooling to enhance internal processes, collaborate on automation opportunities, and maintain AI systems to improve productivity.

The Lead AI Engineer will develop AI and ML systems with a focus on intelligent decision-making and data pipelines to improve agent performance and continuous learning.