Top AI Engineer Jobs

Lead development of spatial deep learning models (diffusion and 3D generative) from data curation through training, evaluation, and production deployment; create scalable training datasets from LiDAR, images, videos, and floorplans; combine geometry algorithms with learned models and integrate outputs into an interactive 3D product stack.

Lead online brand reputation by owning page-one search results, producing SEO-optimized content, building PR relationships, launching customer advocacy, and executing rapid-response campaigns during reputation attacks.

Lead AI engineering initiatives, develop AI systems, integrate LLMs, mentor teams, and collaborate on business requirements within agile frameworks.

The role involves building tools for AI-assisted software development, optimizing workflow, and maintaining secure development environments.

Lead design and development of scalable full-stack applications using Java, Angular, microservices, and event-driven architectures. Integrate and deploy agentic AI/GenAI/LLM solutions for M&A use cases, architect resilient systems, implement CI/CD and cloud/container platforms, promote engineering best practices, and mentor teams across the SDLC while partnering with Architecture, Security, Risk, and Business stakeholders.

Build and optimize conversion-focused web experiences, landing pages, demos, and growth systems. Instrument analytics, attribution, and experimentation; integrate APIs and automate workflows; apply AI to content, personalization, and experiment generation; and partner with Growth, Marketing, and Sales to drive qualified pipeline and measurable revenue outcomes.

Own model selection, evaluation, and deployment for voice and multimodal restaurant AI agents. Build datasets, experiments, and monitoring to improve accuracy, safety, latency, and cost. Partner with product and engineering to ship model changes with guardrails, rollouts, and clear metrics tied to business outcomes.

Build and operate scalable, secure cloud infrastructure for real-time AI services. Improve reliability with SLOs, observability, capacity planning, and failure testing. Implement IaC, CI/CD, deployment safety, secrets and vulnerability management. Partner with product and AI teams, diagnose distributed failures, reduce cost, and automate platform patterns to enable faster, safer releases.

Build end-to-end AI-driven restaurant products from discovery through production. Own full-stack interfaces, backend services, agent workflows, integrations (POS, reservations, telephony), evaluation systems, and instrumentation. Design agent workflows combining models, rules, memory, and human escalation. Work directly with product, design, and restaurant users to iterate, improve reliability, latency, accuracy, and cost, and ship production-quality solutions.

As a Senior AI Platform Engineer, you will build and maintain AI platform infrastructure, enabling scalable access to AI tools and workflows, ensure security and reliability, and define deployment standards.

We seek an AI Research Scientist/Engineer to apply AI techniques in healthcare, focusing on biological simulations and data analysis for NIH-funded projects.

The R&D Engineer will design and implement performance workloads on Cerebras' wafer-scale hardware, collaborating across teams to advance AI and scientific computing. Responsibilities include optimizing performance models, contributing to the technology roadmap, and publishing research findings.

Lead the integration of modern AI practices, provide mentorship, and develop scalable solutions across teams at Riot Games, focusing on enhancing their AI capabilities.

The Software Engineer - AI will design and develop end-to-end generative AI applications, building pipelines and automation for real-world deployment, while contributing to AI/ML development strategies.

Develop and implement manufacturing test solutions for high-performance AI servers, focusing on automated testing, collaboration with teams, and debug analysis.

As an AI Native Forward Deployed Engineer, you'll bridge customer operations and product development, implementing technical solutions and enhancing the platform based on field insights.

As a Software Engineer at Convex, you'll develop APIs and AI integrations, enhance platform usability, and collaborate with the team on various projects to improve developer tools.

Design, build, and operationalize analytics and AI solutions across the enterprise: architect dashboards and data pipelines, deploy ML/LLM models and automation (RPA/low-code), partner with business and IT for deployment and governance, and measure and optimize KPIs to drive data-driven decision-making.

Join a passionate team at Speakeasy as a Product Engineer, focusing on AI control plane. Responsibilities include product ownership, developing secure solutions, and enhancing customer experiences across the web infrastructure.

Ship real open-source integrations and MCP servers to bring SearchApi to agent and automation frameworks. Create PRs, example apps, docs, and integration guides. Stay current with the agent ecosystem, pick high-leverage targets, and work independently to deliver production-ready integrations.

Lead the development and integration of advanced safety systems for scientific AI applications, building a team and establishing a research strategy.

The AI Lab Research Engineer will develop scientific AI models for complex problems, design and deploy AI agents, and collaborate across disciplines in a lab environment.

Design and build AI-powered product features, develop LLM-based systems, ensure quality, collaborate on product decisions, and optimize performance.

Design, integrate, and implement AI-enabled systems using Java/Python/JavaScript, LLMs, RAG, Neo4j and AWS. Build data pipelines, analytics, and natural language/agentic AI interfaces; support cloud deployments and intelligence-focused visualizations.

You'll lead the development of AI-driven systems for a dating platform, focusing on personalized matching algorithms and voice interactions. Responsibilities include designing ML pipelines, deploying models, and conducting experiments to improve user experience.