Top AI Engineer Jobs

Develop experimental AI techniques for agentic marketing applications, especially image and video generation. Build proofs of concept, autonomous evaluation and improvement systems, realistic AI-generated video, and brand-aligned creative generation workflows. The role requires strong quantitative and probabilistic thinking, backend architecture skills, creativity with LLM applications, and product intuition.

The AI Engineer will prototype and implement innovative document search methods, owning impactful projects as part of Humata's cognitive architecture team.

As a Fullstack AI Engineer, you'll prototype frameworks and scale real-time engines, focusing on product feel without handed specs.

As a Senior AI Engineer, you will architect and build AI engines for seamless interactions, define metrics for performance, fine-tune models, and collaborate on product vision.

As a Staff ML Engineer, you will architect and build Spara's AI engine, craft user experiences, collaborate with leadership, and innovate in AI application.

Lead API product direction for search and retrieval primitives, partner with strategic accounts to architect integrations, translate customer insights into product specs and roadmaps, run technical deep-dives and documentation, and prototype and ship production code to improve developer experience and platform reliability.

As an Applied AI Engineer, you will create AI systems for Governance, Risk, and Compliance, focusing on accuracy, innovation, and responsible design.

The AI Engineer will develop AI systems, lead model orchestration and inference, and collaborate on product strategies. Responsibilities include designing architecture, optimizing performance, and mentoring engineers.

As a Founding AI Engineer at Jetty, you'll build systems for intelligent health management, focusing on backend engineering, data pipelines, and context layers for long-term insights.

As a Solutions Architect, you will guide customers in integrating AI coding tools into their workflows, design AI-enhanced development processes, and collaborate cross-functionally to advocate for customer needs while ensuring successful product deployments.

The Founding AI Engineer will design and build an AI-native compliance platform, handling complex financial data and developing scalable backend services, while establishing processes for enterprise readiness.

As an Applied AI Engineer, you will develop and manage full-stack AI projects, build AI infrastructure, and enhance engineering processes to scale solutions at Ramp.

The AI Engineer will develop AI applications and systems, optimize workflows, manage memory and context, and collaborate with teams to integrate AI solutions into products.

Design, implement, validate, and deploy advanced machine learning and statistical models across the data science pipeline. Collaborate with product and engineering, mentor peers, translate research into production, and deliver high-impact business insights using large-scale and distributed data platforms.

Collaborate to enhance product through analytics, conduct data exploration on user behavior, and present insights to influence product decisions.

Design and deploy AI capabilities for U.S. federal agencies, focusing on integrating AI models with government systems, and ensuring ethical standards.

Design and implement AI capabilities for federal agencies, focusing on scalable systems and API integrations while ensuring safety and ethics.

The Investment AI Engineer will design, build, and operate AI workflows for investment processes, collaborating with researchers and portfolio managers to improve automation and efficiency.

The Senior AI Engineer will design and build multi-agent systems, develop agent skills, and collaborate with product teams to solve challenges involving AI-driven applications.

The AI Engineer will drive internal productivity through AI integrations, design multi-agent systems, and enhance workflows across departments. Responsibilities include developing AI-driven solutions and improving operational efficiency.

Lead the Agentic AI team to develop complex multi-agent systems using LLMs, focusing on integration, evaluation, and planning.

Partner with enterprise AI teams to design, implement, and scale production-grade generative AI observability and evaluation programs. Lead technical discussions, build custom integrations and tooling, manage multiple client engagements from scoping to delivery, and drive best practices across cross-functional teams to influence product and expand customer impact.

Productionize detection, tracking, and vision-language models across edge devices and cloud infrastructure. Build evaluation systems, data feedback loops, deployment pipelines, and production metrics; optimize inference cost and performance; run staged rollouts and experiments; and ship customer-facing features connecting inference services, APIs, and product behavior.

Designs, administers, troubleshoots, and improves enterprise identity, AI, and multi-cloud infrastructure platforms. Responsibilities include managing Microsoft Entra ID, authentication, access controls, AI platform governance, Azure/AWS/GCP services, infrastructure automation, monitoring, security, compliance, migrations, and operational resilience. The engineer collaborates with cybersecurity, development, data, and infrastructure teams while independently supporting assigned platforms and developing automation using PowerShell, Microsoft Graph, and cloud-native tools.

Develops cost transparency, dashboards, forecasting, budgeting, governance, and optimization strategies for AI and machine learning workloads. Partners with engineering, data science, finance, procurement, security, and risk teams to improve cloud and vendor spend, GPU utilization, model deployment efficiency, and responsible AI adoption. Automates cost management using scripting, infrastructure-as-code, and cloud-native tooling while evaluating hosted, internal, and open-source AI platforms.