Top AI Engineer Jobs

Build and deploy autonomous, multi-agent AI systems using LangChain, LangGraph, AutoGen, MCP, and A2A. Develop RAG pipelines with Knowledge Graphs, fine-tune language models for edge deployment, and create evaluation frameworks for reliability, safety, and performance. The role also involves production ML engineering, cloud architecture across AWS and Azure, microservices, observability, and collaboration in classified environments.

Build and operate backend systems for Suno’s multi-step AI music agent. Design evaluation datasets, graders, regression sets, release gates, and data pipelines for nondeterministic systems. Create internal tools for reviewing model output, support production launches and on-call operations, and collaborate with ML engineers, product managers, and partner teams. The role also involves mentoring engineers and shipping high-traffic creative music features.

Design and develop AI capabilities for CrowdStrike’s cross-platform endpoint sensor across macOS, Linux, and Windows. Responsibilities include operationalizing machine learning models, building telemetry pipelines for training and inference, integrating features with core OS subsystems, debugging performance and memory issues, and leading architecture, delivery, mentoring, and cross-functional collaboration.

Build and deploy data integrations, AI-powered workflows, and internal tools. Responsibilities include connecting APIs and databases, improving data quality, implementing validation and monitoring, developing retrieval and automation applications, translating business needs into deliverables, deploying production solutions, documenting systems, and transferring knowledge to internal teams.

Own the conversational intelligence layer of a consumer application, including agent architecture, prompt design, tool calling, reasoning, memory, error handling, response rendering, model routing, and evaluation. Build dependable multi-turn language-model experiences, improve search and ranking quality, and launch personalized AI features for live users. The role requires broad applied AI expertise, strong analytical ability, and experience diagnosing production issues.

Design, build, evaluate, and productionize AI solutions for clinical-trial data harmonization, retrieval, and knowledge graphs. Develop auditable RAG pipelines, define systematic evaluation methods, implement human-review controls, and expose capabilities through APIs. Collaborate with architects, data engineers, and product stakeholders to integrate AI into the data-layer platform while meeting regulatory, traceability, and compliance requirements.

Build and own the recruiting marketplace’s AI/ML intelligence layer, including recommendation, ranking, retrieval, and LLM-powered candidate-role matching. Ship production features rapidly, architect data pipelines and model services, and translate founder product goals into deployed systems. The role requires applied machine learning, recommender systems, production LLM API experience, and strong Python or TypeScript skills. Work is fully on-site in San Francisco six days per week.

Build and deploy production AI agents using tool-calling LLMs, structured outputs, API orchestration, and streaming responses. Develop evaluation harnesses, monitor quality and traces, and improve reliability, latency, and cost through caching, token budgets, retries, and observability. Create validated agent tools and integrations while collaborating with product, mobile, and backend teams. Own the full lifecycle from prototyping and evaluation through staging, production deployment, monitoring, and continuous improvement.

Lead the architecture, build, and deployment of AI-driven automation and intelligent systems to solve security, compliance, and risk challenges. Develop real-time intelligence, workflow automation, and risk-based decision-making tools, and engage customers and partners to refine and deploy production-grade solutions.

Prototype and develop generative AI applications for business software, including automated model evaluation, synthetic video, and consistent image and brand asset generation. Own projects from experimentation through product delivery, combining system architecture, quantitative reasoning, product judgment, and creative LLM application development.

Design and deliver scalable, safe conversational AI agents for regulated environments. Coordinate multiple agents, monitor production traces, create evaluation cases, improve agent frameworks, integrate external data sources, and support consistent conversations across voice, text, and email. The role requires hands-on experience shipping conversational agents and AI applications at scale, plus evaluation and tracing infrastructure experience.

Supports AI-enabled automations, integrations, prompt and workflow design, and Claude Enterprise administration while assisting with SharePoint Online and NetDocuments administration. Responsibilities include provisioning, permissions, governance, documentation, Microsoft 365 support, scripting, Power Automate flows, and tier-1/tier-2 issue resolution. The role is designed for an early-career professional with 0–2 years of experience and offers development toward AI engineering or platform administration.

Build and maintain API integrations, data pipelines, web applications, and AI-powered solutions using LLM APIs. Support databases, deployments, debugging, testing, authentication, and application security. Improve UI/UX, evaluate AI-generated outputs, investigate emerging technologies, and contribute to workflow improvements. The role is suited to current students, recent graduates, or candidates with equivalent practical experience and emphasizes hands-on AI development, collaboration, and continuous learning.

Leads the design, development, and production deployment of AI solutions for healthcare payer applications. Responsibilities include architecting AI models and integrations, developing scalable production systems, evaluating model accuracy and fairness, translating business workflows into AI-enabled automation, researching emerging techniques, advising stakeholders, and mentoring junior engineers.

Owns the implementation, hardening, tuning, and polish of game AI behaviors and systems for shipping quality. Collaborates with design, animation, and other disciplines to create believable enemy behavior and strong player experiences. The role requires expertise in Unreal Engine, modern game AI architecture, animation systems, and C++, along with experience shipping at least one game focused primarily on AI, preferably a AAA action or FPS title.

Design, build, deploy, and optimize AI/ML and generative AI solutions on Databricks. Develop scalable Spark and Delta Lake data pipelines, operationalize models with MLflow, and build RAG and LLM applications. Implement MLOps practices including CI/CD, monitoring, governance, and model deployment. Collaborate with technical, data, engineering, and business teams to deliver enterprise-scale AI platforms and analytics solutions.

Develop and implement AI solutions and machine learning systems that enhance products and solve complex client problems. Responsibilities include advanced analytics, LLM production deployment, prompt engineering, RAG pipeline optimization, cross-functional collaboration, technical discovery, responsible AI advocacy, client relationship building, and mentoring junior team members.

Leads pre-sales technical discovery and solution engineering for Cloudflare’s AI and startup customers. Partners with sales teams and technical founders, maps business and AI initiatives to Cloudflare’s Developer Platform, delivers demos and proof-of-concepts, develops reference architectures, and builds technical champions. The role requires strong knowledge of serverless, edge, networking, security, cloud, and AI application patterns, plus the ability to communicate with developers and executives. Up to 50% travel is required.

Build, deploy, and operate production voice AI agents integrated with telephony and contact-center platforms. Design autonomous call workflows, author and version agent instructions and prompts, manage context and interruptions, and create automated evaluation for probabilistic voice behavior. Monitor safety, latency, containment, hallucinations, transcription accuracy, and tool usage. Work directly with customers in forward-deployed environments, translate requirements into testable specifications, and collaborate with product, design, and engineering teams.

This role involves developing and maintaining CI/CD pipelines, enhancing deployment processes, ensuring infrastructure reliability, and promoting best practices within the AIDR platform team.

Design and build foundational web services, service mesh infrastructure, developer tooling, CI/CD automation, and highly reliable distributed systems. Architect scalable APIs and data consistency patterns, optimize high-throughput systems, establish observability and reliability practices, and mentor engineers. The role supports millions of transactions across mobility products and requires collaboration across application, infrastructure, and advanced technology teams.

Develop and support scalable full-stack software using Java, Spring Boot, React or Angular, Python, APIs, microservices, and cloud technologies. Lead architecture, AI-enabled capabilities, testing, troubleshooting, observability, automation, and performance improvements. Mentor engineers, conduct code reviews, influence technical direction, and independently deliver complex solutions across the software lifecycle.

Develop and support complex full-stack software solutions using Java, Spring Boot, React or Angular, and Python. Design scalable APIs, microservices, distributed systems, and cloud-based architectures while integrating AI and machine learning capabilities. Lead technical implementations, testing, troubleshooting, performance improvements, and operational readiness. Mentor engineers, conduct code reviews, influence architecture and engineering standards, and independently deliver complex features with minimal oversight.

Build and operate production-grade full-stack applications with AI as a core capability. Develop custom AI agents, MCP integrations, RAG systems, and AI-enabled engineering workflows. Own features end to end, including architecture, implementation, testing, deployment, observability, security, reliability, and lifecycle support. Collaborate with business and engineering teams while applying cloud-native practices, scalable system design, and rigorous evaluation of AI-generated outputs.

Architects and develops systems software for emerging memory and storage technologies, with emphasis on AI inference stacks and heterogeneous computing. Responsibilities include Linux application and library development, emulator creation, system-level debugging, accelerator programming, performance evaluation, technical leadership, cross-functional collaboration, mentoring, and presenting technical findings. The role requires expertise across AI frameworks, inference engines, operating systems, kernels, hardware interfaces, memory hierarchies, and distributed serving systems.