Top AI Engineer Jobs

Create AI-generated visuals and interactive prototypes using generative tools and front-end prototyping. Collaborate with designers and engineers to iterate concepts, document workflows, and push generative design and interaction approaches.

Design, deploy, and optimize cloud-native ML systems on AWS, integrate foundation models (Bedrock/Anthropic), build and monitor SageMaker pipelines and data pipelines, detect drift, extend ML infra and libraries, and collaborate cross-functionally to deliver secure, cost-efficient production ML services.

The Senior AI/ML Engineer will design, develop, and deploy scalable ML systems focusing on LLM applications, collaborating with various stakeholders to enhance healthcare data insights.

Own and improve Rime's React/TypeScript web app, build conversion and onboarding flows, architect analytics pipelines and instrumentation, implement growth-oriented features, and partner with product, marketing, and sales to measure and optimize user funnels and acquisition.

The AI Support Engineer will troubleshoot complex customer issues, utilize emerging AI technologies for support, and improve operational processes in a hybrid team environment.

Build end-to-end internal applications for Finance and Supply Chain: design React/TypeScript UIs, Python services/APIs, durable workflows, and enterprise integrations (Oracle Fusion, Zip). Own product lifecycle from discovery to rollout and production support, ensuring reliability, security, auditability, and reusable building blocks; apply AI where valuable.

Design, build, and maintain AI platform infrastructure and services. Implement scalable backend systems, REST APIs, CI/CD, observability, automated testing, and cloud-native streaming. Collaborate across teams, apply TDD, and use generative AI tools to accelerate development.

Early-career DevOps role focusing on Kubernetes and AWS operations, embedding security into the development lifecycle, automating processes, and implementing CI/CD solutions. Remote, fixed-term accelerator program for students and recent graduates.

Early-career TechOps role: build dashboards/alerts, script repetitive ticket steps, participate in RCAs, log user pain points and propose fixes, create a lightweight agent (e.g., Telegram bot) with reusable skills, optimize incident-management workflows, and write actionable runbooks to improve support-driven user experience.

Design and build developer-facing AI platform tools, define end-to-end AI development lifecycles and guardrails, deliver reusable tooling and integration patterns, drive cross-functional adoption, mentor engineers, and enable secure, scalable LLM-powered applications.

Lead architecture and adoption of enterprise AI and agentic systems: design multi-agent orchestration, integrate LLMs securely, define CI/CD and reliability standards, lead threat modeling and chaos testing, build reusable platform tooling, mentor senior engineers, and drive cross-team roadmaps and stakeholder alignment.

Design, build, and deploy production-ready agentic AI and multi-agent systems to automate enterprise workflows. Integrate agents with enterprise platforms (APIs, OAuth2, webhooks), prototype solutions, implement guardrails and observability, and deliver scalable automation primarily for marketing operations. Collaborate with clients and cross-functional teams, provide technical leadership, and produce reusable integration and orchestration patterns.

Architect and build production-grade AI applications and shared platform services across desktop, mobile, web, and cloud. Lead full-stack engineering, CI/CD, observability, and operational excellence. Build foundations like agent orchestration, memory/context services, evaluation frameworks, telemetry, and workflow execution. Operationalize NVIDIA AI technologies, collaborate with cross-functional teams, mentor engineers, and drive architectural alignment to deliver scalable, reliable enterprise AI products.

As a Senior AI Developer Technology Engineer at NVIDIA, you'll design and optimize AI and HPC workloads, solve performance issues, and influence future hardware and software design.

Design and develop a C++-based system for simplifying and accelerating computing in DL and HPC, alongside evaluating performance and improving library quality.

The role involves researching and developing GPU-accelerated AI workloads, optimizing algorithms, and collaborating with technical experts to improve hardware performance.

Design, build, and deploy ML models (NLP, time-series, event detection) and LLM-driven systems; develop low-latency model serving, inference pipelines, CI/CD, monitoring, and retraining on AWS; collaborate with data engineers to produce training datasets, feature stores, and scalable production microservices in Python and Go.

Design and implement hardened Kubernetes integrations and automation pipelines for hybrid-cloud AI workloads. Provide operational support, troubleshoot production systems, collaborate with customers and engineering teams, and contribute to open-source projects to scale AI infrastructure.

Lead design, development, and production deployment of intelligent agent systems that plan and execute tasks autonomously. Build scalable architectures, integrate ML/LLM models, automate workflows, design evaluation metrics, and implement APIs. Collaborate with researchers, data scientists, and engineers, produce technical documentation, and ensure compliance, security, and reliability of agentic solutions across products.

Lead design and delivery of Pave's AI compensation analyst: build production LLM-based agentic systems that reason over compensation data, implement retrieval and evaluation frameworks, drive 0→1 technical initiatives, and align engineering and product to ship autonomous AI features into production.

PowerPay seeks a Software Engineer to manage development tasks from requirements to deployment, focusing on backend services and AWS infrastructure. Responsibilities include writing Python and SQL, utilizing AI tools, maintaining data pipelines, and ensuring communication with stakeholders.

Hands-on staff full‑stack engineer who designs, builds, tests, deploys, and operates frontend and backend systems, integrates LLMs and AI tooling, implements RAG and prompt engineering, drives CI/CD and cloud infrastructure, troubleshoots production issues, and mentors teams to adopt AI‑augmented development practices.

Design, analyze, test, and deliver high-reliability mechanical hardware for a large satellite constellation. Own product lifecycle from concept through high-volume production and on-orbit operation, drive DFM, perform environmental testing, and collaborate on system-level requirements and optimization.

Lead development and deployment of bandit models and large-scale neural networks for monetization. Collaborate cross-functionally, drive product and business decisions, mentor engineers, and ensure scalable, high-quality ML solutions across the ML stack (training, fine-tuning, evaluation, deployment, and monitoring).

Design, train, evaluate, deploy, and monitor large-scale ML systems (LLMs and bandits) for Duolingo Monetization. Collaborate cross-functionally, drive product decisions, mentor engineers, and ensure scalable, high-quality solutions.