Top AI Engineer Jobs

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.

As an AI/ML Engineer, you will design, train, and deploy ML models for flight data analysis, providing actionable performance feedback for pilots.

As a Software Engineer for Babbix, you'll design and build AI-driven features, improve developer experience, and collaborate with clients to enhance the insurance submission process.

The Research Engineer will design and implement innovative approaches to marketing measurement, build production systems, and develop algorithms to solve complex marketing attribution problems.

As a Software Engineer, you'll develop AI scheduling agents to coordinate meetings across various complexities, improve architectures, and analyze agent performance.

As an AI/ML Engineer, you will design and deploy models for real-world applications, optimize data pipelines, and engage with global leaders to enhance AI technology's impact across sectors like healthcare and government.

The AI Platform Engineer will develop full-stack systems connecting AI models to real-world applications in drug design, collaborating with scientists and ensuring rapid experimentation cycles.

The AI Platform Engineer manages Oracle Cloud Infrastructure and supports AI agent deployment, DevOps, automation, and security for ERP Suites.

The role involves leading the design and delivery of multi-agent systems for underwriting and claims automation, ensuring production readiness and business alignment.

The role involves developing, training, and deploying machine learning models, building data pipelines, and collaborating with teams for integration and performance optimization. It supports the ML lifecycle from data collection to monitoring.

The Senior Software Engineer will design and build full-stack applications using React and Java, ensuring scalable systems, mentoring juniors, and collaborating with cross-functional teams.

Develop AI-driven solutions for insurance operations including web apps, dashboards, and systems integration while ensuring compliance and data security.

The Automation Engineer designs, develops, and maintains data pipelines and API integrations for scientific workflows while ensuring compliance with industry standards. The role involves collaboration with scientists and the use of various technologies to optimize data processes.

Lead design, development, and deployment of production-grade AI systems (LLM apps, retrieval pipelines). Own technical direction, ensure scalability, reliability, security, and responsible AI. Drive MLOps, model evaluation, monitoring, and mentor engineers while partnering with product and business stakeholders.

Lead the design of advanced AI systems for healthcare, develop evaluation frameworks, and drive LLM development while shaping AI strategy in a collaborative environment.

As an AI Process Forward Deployed Engineer, you will deploy enterprise workflows, improve systems, drive adoption, and collaborate across teams to enhance AI integration in business processes.

Design, build, and run large-scale backend and data infrastructure; productionize and run experiments on AI/ML models; implement MLOps best practices (versioning, monitoring, alerting); improve model accuracy, speed, and cost; and drive engineering process and culture improvements.

Develop, design, and maintain AI/ML software solutions; collaborate with engineers and leadership; build tools to analyze large-scale data; debug, profile, optimize, and test software; create documentation and user support materials; leverage modern LLM tools to accelerate development.