Top AI Engineer Jobs

Lead architecture and delivery of LLM-powered data platforms and pipelines: data extraction, transformation, storage, model integration, deployment and evaluation. Drive scalability, reliability, cost-efficiency, engineering standards, and cross-functional collaboration. Mentor engineers, lead architecture reviews, prototype and productionize ML capabilities, and ensure operational maturity (SLOs, observability, on-call). Translate business goals into data products for enterprise customers.

Design, build, deploy, and scale production AI applications and agents. Translate business problems into scalable AI solutions, develop backend services and APIs, integrate with enterprise data platforms, ensure observability, governance, and MLOps, and collaborate with cross-functional client teams.

Design, build, and ship AI-native features and production LLM agents for healthcare workflows. Improve core AI stack (prompt orchestration, retrieval, evaluations), implement safety/guardrails, and deliver full‑stack solutions touching backend infrastructure and member-facing experiences.

Design, build, deploy, and operate production-grade AI systems within delivery teams. Select appropriate ML/DL algorithms, define evaluation methodology, implement CI/CD and IaC, ensure observability and cost controls, and co-hand off operable systems to receiving teams while communicating technical trade-offs to stakeholders.

Design, develop, train, and evaluate state-of-the-art generative AI models (GANs, VAEs, Transformers) and large-scale recommender systems (GNN embeddings). Optimize model performance and scalability, integrate solutions with cross-functional teams, provide technical leadership, and mentor junior engineers to drive AI-driven media buying decisions.

Design, develop, and deploy advanced AI/ML models; optimize pipelines for performance and scalability; integrate solutions with cross-functional teams; research new techniques; mentor junior engineers; monitor and maintain production AI systems.

Architect and implement scalable generative AI solutions for enterprise document workflows. Own end-to-end delivery, collaborate with product and commercial teams, build extraction and automation features, ensure privacy and security by design, mentor engineers, and drive customer adoption of core platform capabilities.

The Principal Edge AI Firmware Engineer will develop and optimize embedded software for AI applications, focusing on performance and energy efficiency. Responsibilities include enhancing AI runtimes, profiling tools, and ensuring customer integration across embedded environments.

Lead applied research and engineering for feedback-driven agentic AI: define reward modeling and reinforcement learning roadmap, design production learning loops and evaluation frameworks, run large-scale experiments, and ship research into production while partnering across product and engineering.

The Senior AI Product Engineer will develop and optimize AI product features, ensuring a seamless user experience through collaboration with engineering teams. Responsibilities include building full-stack features, implementing user feedback mechanisms, and monitoring product performance.

Build and lead development of Klaviyo's conversational AI platform: native chat surface, agent orchestrator, and agentic UI. Design scalable, reliable systems, ship high-quality code, mentor engineers, drive technical decisions, and improve AI-powered customer experiences.

Build and operate Coinbase's LLM and agent platform: design the LLM gateway, agent runtime, knowledge bases, AI spend governance, observability/eval tooling, and ship production agents while partnering cross-functionally to ensure secure, cost-effective, and responsible generative AI at scale.

Design and implement algorithms for packing and allocation in logistics, ensuring operational feasibility. Manage data and integrate solutions into workflows.

As an AI Engineer, you will design and implement systems that enforce feasibility and authority in logistics decision-making, ensuring compliance with command policies. You will work on integrating AI reasoning with execution workflows, handling data validation, and improving the decision-making framework.

Build and operate production agentic AI systems and LLM-powered features for financial services. Implement RAG pipelines, orchestration, and shared AI infrastructure; monitor, debug, and improve reliability. Collaborate with product and design, write production code, and follow security, explainability, and compliance best practices in a regulated environment.

Build and operate production-grade agentic AI systems and LLM-powered customer features. Design RAG pipelines over sensitive financial data, contribute to shared AI infrastructure (LLM services, agent orchestration, evaluation/monitoring), and own reliability, security, and compliance in production. Collaborate with product and design to deliver customer-focused AI solutions.

Lead development of AI-native production systems: convert AI-assisted prototypes into scalable, secure software; own architecture, reliability, and technical direction; direct and control AI coding agents; define engineering standards and workflows; collaborate with founders on product and strategy; debug complex issues and elevate engineering quality.

The Founding Staff AI Engineer will design and implement production-grade AI systems focused on document intelligence in the insurance sector, collaborating with co-founders to shape the architecture and roadmap for AI solutions.

The role involves building AI systems for customer support, creating automated insights, and operationalizing AI in workflows while collaborating with engineering and customer solutions teams.

Design, deploy, and maintain on-prem AI infrastructure and agentic systems. Build GPU clusters, model serving, vector DB-backed RAG pipelines, fine-tune and deploy models, implement governance (MCP), and automate CI/CD and monitoring to integrate AI capabilities into enterprise workflows.

Design and build the central AI orchestrator (Maestro) and distributed agents; own containerized deployment, observability, LLMops, evaluation pipelines, RAG optimization, and technical leadership for agentic production systems.

The Senior Customer AI Engineer will support enterprise customers' AI Marketing efforts, focusing on integrations, technical solutions, and optimizing customer outcomes while maintaining effective communication across teams.

Build and maintain backend, CI/CD, and data integrations for the NOS platform. Ramp on Palantir Foundry and AI/data tooling, write production code and tests, and collaborate with engineers and data scientists under senior review to deliver scoped features.

Build automated unit, integration, and end-to-end tests for NOS using AI-assisted generation and review. Translate NQA-1 regulatory requirements into traceable test artifacts and evidence. Establish defect tracking and V&V workflows, ramp on Palantir Foundry and NOS architecture, and help develop testing approaches for AI-powered features, including prompt regression and output validation.

Design and build full-stack AI prototypes; decompose products into model, retrieval, prompting, agents, and orchestration components; integrate APIs, databases, and cloud; define evaluation loops; perform error analysis; collaborate cross-functionally; and present results to founders and partners.