Top AI Engineer Jobs

Build and deploy AI agents, intelligent applications, scalable backend services, APIs, and full-stack solutions. Integrate large language models, AI services, data platforms, and external APIs into customer-focused products. Collaborate with engineering, product, and cross-functional teams while contributing to cloud-native applications, data pipelines, distributed systems, testing, deployment, and production operations.

Lead test automation for web, mobile (iOS/Android) and REST APIs; build and expand automation frameworks (Appium/Selendroid), run CI/CD regression, perform root-cause for defects and performance, drive QE standards, mentor QE staff, and ensure enterprise-scale SaaS quality and scalability.

Lead test automation for web, mobile (iOS/Android) and REST APIs; build and maintain automation frameworks (Appium/Selendroid), run CI/CD regression suites, perform root-cause analysis, drive QE standards, mentor QE team, and ensure performance, scalability, and reliability for enterprise SaaS.

Designs, operates, automates, troubleshoots, and optimizes GPU, AI, HPC, storage, networking, and research computing infrastructure. Supports researchers using machine learning, simulation, and data-intensive workloads; applies DevOps and infrastructure-as-code practices; improves system performance, reliability, security, documentation, and user workflows. Collaborates with technical teams, researchers, and vendors to diagnose complex issues and develop scalable solutions.

Designs, operates, automates, troubleshoots, and optimizes GPU, AI, HPC, storage, networking, and software infrastructure supporting university research. The role administers multi-GPU systems, maintains Linux environments, develops Bash and Python automation, supports AI/ML workloads, improves infrastructure-as-code and DevOps practices, and collaborates with researchers, vendors, and technical teams. Responsibilities also include monitoring, security, documentation, compliance, user support, and evaluating tools for large-scale model development, training, inference, and data movement.

Develop spatial AI and perception systems for autonomous mobile robots operating in dynamic warehouses and yards. Train and evaluate models using real multimodal fleet data, improve 3D scene representations and state estimation, and develop evaluation methods. Take research concepts from literature through on-vehicle validation and productionization, collaborating with software and test engineering on data, annotation, calibration, monitoring, and simulation infrastructure.

Design, develop, test, and deploy AI-powered features and tooling for the CORI robotic surgery platform using ML, generative AI, computer vision, and AI-assisted developer tools. Build integrations, services, APIs, automation, and CI/CD pipelines while ensuring cybersecurity, privacy, quality, and regulatory compliance. Collaborate on architecture, prototyping, evaluation, deployment, and continuous improvement across robotic software and cloud systems.

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.

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.

Build and evaluate LLM-powered agentic systems for security operations: implement features, create evaluation frameworks, automated benchmarks and regression tests, investigate failures, experiment with prompting/retrieval/planning, build infrastructure for deployment and monitoring, and collaborate with customers and product teams to improve agent performance and reliability.

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.

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.

As a Senior AI Engineer, you'll design and deploy AI systems, own AI pipelines, and integrate LLMs into workflows, significantly impacting product direction.

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.

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.

Build and own an agentic creative pipeline that generates, vets, and optimizes thousands of image and video ad variants daily: extend LLM critic feedback to multimodal assets, build fast cost-efficient video pipelines, route generative models by cost/quality, train and deploy brand-style checkpoints, implement policy pre-flight checks, and ship event-triggered ad pipelines with A/B validation in production.

Lead enterprise data science strategy and deliver predictive and prescriptive models for forecasting, asset performance, predictive maintenance, and portfolio optimization. Partner cross-functionally to prioritize analytics initiatives, translate technical findings for leadership, evaluate emerging AI/ML technologies, and support data infrastructure, ETL, and BI capabilities to drive data-informed decisions across the renewable energy portfolio.

Develop and implement AI features, integrate LLM APIs, collaborate on system design, and mentor junior engineers in a production environment.

Senior technical leader responsible for the success of complex enterprise AI deployments: setting architecture and evaluation strategy, ensuring production reliability, coaching and developing engineering leads, partnering with customer executives, debugging critical issues, and scaling pilots to enterprise deployments while using AI-native engineering practices.

Design, build, deploy, and operate production AI systems for customers. Implement end-to-end AI workflows (models, prompts, agents, retrieval, evaluators), write maintainable Python services, monitor and debug live systems, create evaluation and observability tooling, and work closely with stakeholders to iterate systems for reliability and business value.

Lead technical delivery and architecture of production AI systems for enterprise customers. Mentor engineers, integrate models/agents with customer data and APIs, operate and improve live systems, measure and debug failures, and drive pragmatic, reliable deployments that deliver business outcomes.

Design, build, and deploy agentic multi-agent AI systems and RAG pipelines for document intelligence and procurement workflows. Integrate and evaluate LLMs, implement orchestration frameworks, build human-in-the-loop approvals, and collaborate on production deployment, observability, and secure architectures for government acquisition environments.

Citizen Health seeks AI/Machine Learning Engineers to develop AI solutions for healthcare using advanced ML techniques, focusing on rare diseases and patient care. Responsibilities include designing ML models, optimizing LLMs, and ensuring compliance.

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.