Top AI Engineer Jobs

Design, develop, and deploy production-grade multi-agent AI systems for financial pricing and hedging platforms. Build RAG pipelines, vector database integrations, planning and reasoning capabilities, resilient agent architectures, APIs, and microservices. Optimize systems for latency, throughput, reliability, and cost. Apply AI research to financial workflows, provide technical leadership, define AI strategy, and mentor junior engineers.

Own the technical direction for generative AI, multimodal, and voice-agent systems used in medical devices. Design deterministic agent architectures, safety guardrails, evaluation frameworks, and traceability processes for regulated clinical environments. Develop and optimize ASR, TTS, language, and multimodal models for edge and cloud deployment under latency and memory constraints. Lead model selection, validation, documentation, cross-functional alignment, code reviews, and mentoring.

Design and implement large-scale, AI-driven SD-WAN and AIOps systems. Develop fault-tolerant, real-time, message/event-driven services in C++/Python/Go. Drive feature design, code and design reviews, testing, debugging, optimization, and end-to-end delivery while collaborating with product teams.

Provide technical support to government teams developing and evaluating AI applications. Advise on LLM, RAG, agentic workflow, orchestration, architecture, integration, evaluation, reliability, observability, guardrails, and secure AI practices. Develop prototypes, technical guidance, recommendations, and stakeholder briefings while assessing emerging frameworks and mission-use-case suitability. Collaborate with engineers, product leads, mission users, and government leadership on operationally relevant AI solutions.

Lead the architecture and implementation of generative AI, multimodal, and voice-agent systems for medical devices across edge and cloud environments. Develop deterministic agent architectures, safety guardrails, evaluation frameworks, speech and language components, and model optimization strategies under strict latency, memory, and reliability constraints. Establish metrics, traceability, and documentation for regulated development, while mentoring engineers and coordinating with device software, clinical, validation, quality, regulatory, and product teams.

Leads research, design, development, testing, validation, and deployment of AI/ML prototypes for space and missile defense. Collaborates with customers and multidisciplinary stakeholders to define requirements and present solutions, mentors junior engineers, tracks milestones and budgets, supports proposals and thought leadership, and advances scalable AI/ML algorithms using large datasets.

Leads research, design, development, testing, validation, and deployment of AI/ML prototypes for space and missile defense. Interfaces with customers and stakeholders, translates mission requirements into technical solutions, mentors junior engineers, tracks milestones and budgets, supports proposals, and contributes to technical thought leadership. The role requires Python-based algorithm development, AI/ML expertise, large-scale data experience, and eligibility for a U.S. Government Security Clearance.

Build and maintain mathematical optimization models for network planning, capacity allocation, routing, and freight operations. Develop production AI applications for process automation, document extraction, and decision support. Establish evaluation, validation, monitoring, and regression-testing frameworks for optimization and AI systems. Identify new business opportunities, prototype solutions, quantify impact, and scale successful initiatives. Partner with operations, pricing, sales, finance, engineering, and leadership to communicate trade-offs and deliver reliable analytical systems.

Own and scale Cobalt’s B2B SaaS go-to-market technology ecosystem, including Salesforce, HubSpot, customer success platforms, integrations, data enrichment, governance, workflow automation, and AI-enabled solutions. Translate business requirements into scalable systems across Marketing, Sales, Customer Success, and Revenue Operations. Manage architecture, data quality, monitoring, permissions, releases, documentation, and technical initiatives from design through optimization.

Design, develop, and deploy production-grade multi-agent AI systems for financial pricing and hedging platforms. Build RAG pipelines, vector database integrations, knowledge graph reasoning, planning and memory systems, and resilient agent architectures. Integrate LLMs, predictive models, APIs, tools, and microservices while optimizing latency, throughput, and cost. Apply AI research to financial workflows, collaborate across engineering teams, and mentor engineers on AI development practices.

Design and deploy computer vision and deep learning solutions for identifying, counting, and tracking retail merchandise. Build image-processing and vision pipelines, train object detection and classification models, develop annotation and synthetic-data workflows, and optimize AI systems for embedded edge hardware. Support customer-facing AI products, collaborate with robotics engineers and vendors, enhance robotic solutions, and create testing methodologies. The role is onsite in Lexington, Kentucky and requires strong technical communication.

Leads Snowflake’s AI Developer Experience engineering team, setting the roadmap for transforming software development and delivery through AI. Responsibilities include managing and developing engineers, improving code review latency, enabling access to modern AI tools and models, establishing agentic development pathways, and maintaining operational excellence through on-call management, incident response, postmortems, and support prioritization. The role requires strong technical judgment, cross-functional communication, measurable customer impact, and experience modernizing developer productivity and CI/test workflows.

Build and deploy AI solutions for clients, including agentic workflows, RAG systems, structured extraction, OCR and document-processing pipelines, retrieval systems, evaluation harnesses, and lightweight interfaces. Translate ambiguous client problems into production code, drive engagements from discovery through deployment, collaborate with stakeholders, and contribute to internal platform capabilities and engineering practices.

Work directly with enterprise customers to design, build, and deploy production voice AI applications. Integrate Retell APIs/SDKs, develop conversational workflows, debug telephony/SIP and infrastructure issues, optimize AI behavior and prompts, lead technical onboarding, and drive product improvements from proof-of-concept to production.

Build and maintain the TypeScript-based Duo Client SDK, including Node.js core packages, public APIs, browser-compatible modules, Vue 3 client components, and platform adapters. Responsibilities include migrating consumers from the GitLab Language Server, managing versioning and deprecation paths, strengthening credential security, optimizing browser performance, writing tests, conducting code reviews, and participating in on-call support.

Leads the design, development, deployment, and monitoring of production-grade AI systems. Builds RAG pipelines, generative AI and conversational agents, multi-agent orchestration, automation workflows, forecasting models, and anomaly detection solutions. Owns the full AI lifecycle from data preparation through deployment and evaluation while optimizing performance, fairness, robustness, and explainability. Collaborates with technical and product teams to deliver AI-powered business solutions.

As a Staff Software Engineer, you'll design, build, and test Gusto's core platform services, mentor engineers, and collaborate with various teams to solve complex engineering problems.

Design and build AI Gateway systems for high-volume, latency-sensitive inference at the edge. Implement APIs, routing, observability, controls, developer workflows and tooling. Prototype ambiguous ideas to production, improve reliability, performance, cost-efficiency and debuggability, and mentor engineers while dogfooding AI-native tools, agents, and automation to accelerate development and operations.

The AI Applications Engineer will prototype AI-driven solutions, collaborate with stakeholders, and create reusable components to address business challenges.

As an AI Product Engineer, you'll build and scale AI-powered features for Notion's Custom Agents, collaborating with teams to enhance user experience and implementing cutting-edge technologies.

Lead design and delivery of enterprise integrations, automation, and AI-driven workflows across SaaS platforms. Architect scalable APIs, middleware, and conversational self-service experiences; partner with Security, Infrastructure, and Product; mentor engineers and drive long-term platform strategy, governance, and operational excellence.

Lead design and delivery of enterprise integrations, automation, and AI-enabled workflows across SaaS platforms. Architect scalable APIs, conversational IT/self-service experiences, and agentic LLM workflows while partnering with Security, Infrastructure, and Product to ensure governance, reliability, and operational excellence.

Lead a team and portfolio building full-stack, cloud-native microservices and platforms. Collaborate with product managers to deliver scalable AWS solutions, mentor engineers, experiment with new technologies, and ensure regulatory needs are met using languages like JavaScript, Java, TypeScript, Python, Go, and container orchestration (Docker, Kubernetes).

Build and productionize reliable, scalable agentic AI systems (orchestration, sandboxed execution, latency optimization, agent memory, self-reflection, knowledge graphs, multimodal I/O). Implement frontier AI algorithms, ship production-grade code, mentor engineers, collaborate with ML, security, product, and UX teams, and drive roadmap and reliability improvements.

Design, evaluate, and productionize agentic AI systems and LLM-driven components to improve agent behavior, reliability, latency, and user-facing experiences. Build eval benchmarks, tune models, manage data quality, and collaborate with platform teams to deploy scalable, safe, and high-performing AI features.