Top AI Engineer Jobs

Build and operate AI-native, full-stack products for sales, marketing, and revenue operations teams. Develop agent workflows, automations, integrations, and production applications for research, enrichment, account scoring, and other go-to-market needs. Collaborate with nontechnical users to understand workflows, test solutions, and improve products. Integrate systems such as Salesforce, Marketo, and Gong while making pragmatic build-versus-buy decisions.

Lead assembly, integration, and testing of satellite payloads, spacecraft buses, and subsystems. Develop test plans, procedures, and automation scripts; execute functional and environmental tests; troubleshoot integration issues; document results; and coordinate with engineering and program teams to ensure spacecraft meet requirements and launch readiness milestones.

Build and operate production AI agents, companions, and workflow automations across sales, finance, marketing, and property operations. Integrate Azure, Snowflake, SaaS platforms, and vendor APIs while managing evaluations, monitoring, security, human review, cost, and reliability. Partner with business users and executives to identify high-value opportunities, measure adoption and outcomes, train users, and maintain governance. The role requires strong backend engineering, applied LLM experience, integration expertise, and sound data-security judgment.

Design, implement, and evaluate novel machine learning and deep learning algorithms, develop scalable research tools, benchmark emerging techniques, and transition research prototypes into production AI systems. The role requires collaboration with research engineers, experimentation across data, architectures, training, evaluation, and deployment, and communication of findings to leadership. Focus areas include transformers, multimodal foundation models, diffusion policies, fine-tuning, distillation, mixture-of-experts, and potentially reinforcement, self-supervised, imitation learning, and robotic VLA models.

Lead hands-on architecture, development, deployment, and optimization of enterprise GenAI, RAG, Agentic AI, and Neo4j Knowledge Graph solutions. Build data pipelines, APIs, microservices, and cloud-native AI infrastructure; rapidly deliver customer POCs and production systems. Serve as the primary technical interface for enterprise clients, troubleshooting performance, scalability, reliability, and cost issues while providing technical consulting.

Develop and enhance LangSmith frontend features using React and TypeScript. Build reusable components, translate designs into high-quality interfaces, optimize performance across browsers and devices, and collaborate with backend, full-stack, and UX/UI teams to improve usability and product quality.

Build and operate Kubernetes-based AI/ML infrastructure, including GPU scheduling, multi-tenant isolation, platform services, infrastructure as code, GitOps, CI/CD, observability, and reliability practices. Deploy reproducible platforms in cloud, restricted, and disconnected environments. Develop runbooks, support incident response, contribute to open-source infrastructure projects, and collaborate with security engineers and government stakeholders. The role requires production infrastructure experience, cloud expertise, programming skills, and eligibility for a U.S. security clearance.

Own ambiguous, cross-team product engineering problems from framing through launch and iteration. Partner with product, design, and data; lead architecture, reliability, performance, and technical decisions across the stack. Influence teams without formal authority, mentor engineers, improve engineering practices, use metrics and experimentation, and apply AI throughout software development. Participate in on-call support.

Develop and maintain Retrieval-Augmented Generation pipelines, LLM integrations, REST APIs, and self-hosted LLM deployments. Conduct LLM alignment red teaming, independently research emerging AI/ML methods, and develop advanced AI applications. The role requires strong Python and AI/ML framework expertise, with preferred experience in model evaluation, fine-tuning, inference optimization, vector databases, AWS, Docker, and scalable data architectures.

Own Matter Health’s AI platform strategy, architecture, governance, budget, and operations. Design secure tools and integrations for deploying AI agents and workflows, including MCP and RPA integrations. Establish development standards, monitoring, reliability, and incident-response practices while partnering with engineering, security, compliance, clinical, and operational teams. Evaluate vendors and platform investments, guide internal builders, resolve complex technical issues, and measure business impact in a regulated healthcare environment.

Designs, secures, automates, and operates AWS GovCloud infrastructure, Kubernetes platforms, CI/CD pipelines, and infrastructure-as-code deployments. Integrates security scanning, compliance controls, monitoring, and incident response into production operations. Supports secure deployment and operation of AI and LLM applications using services such as Amazon Bedrock, collaborating with software, AI, cloud, and cybersecurity teams.

Designs, builds, and operationalizes production AI solutions for automotive applications, including LLM applications, RAG pipelines, intelligent agents, predictive models, and generative AI tools. Develops full-stack, cloud-native systems, prototypes AI capabilities, improves prompts and model performance, and integrates enterprise platforms. Collaborates with engineering, product, and data teams while ensuring governance, privacy, security, reliability, and observability standards.

Develop scalable AI-powered Salesforce solutions using Agentforce, Data 360, Apex, Lightning Web Components, and JavaScript. Responsibilities include building agents, workflows, automations, data architectures, integrations, and customer solutions; collaborating with project teams; explaining technical concepts to stakeholders; maintaining Salesforce certifications; and supporting evolving AI technologies. The role includes up to 25% travel for customer projects and company events.

Designs, develops, tests, and productizes scalable AI/ML algorithms and models. Responsibilities include model architecture, data pipeline integration, metrics analysis, deployment, scheduling, monitoring, retraining, and iterative improvement. The engineer will support mission-critical government systems, apply software engineering and model evaluation practices, communicate with customers, and potentially lead geographically distributed teams. The role requires advanced machine learning expertise, strong analytical and communication skills, U.S. citizenship, and an active or reinstatable TS/SCI with Polygraph clearance.

Develops and evaluates artificial intelligence and machine learning algorithms for U.S. Government mission systems. Responsibilities include selecting and analyzing data, training and improving models, conducting testing and validation, overseeing system integration, researching AI/ML methods, converting prototypes into scalable solutions, and verifying data and model quality. The role requires collaboration, independent work, and support for critical software and systems under a government contract.

Develops and deploys AI solutions that automate business processes, improve decision-making, and support innovation. Responsibilities include building AI prototypes and applications, tuning models, analyzing data, integrating enterprise systems, developing generative AI and intelligent search solutions, improving data quality, and partnering with business and IT teams. The role also evaluates AI technologies, documents recommendations, and promotes responsible AI practices involving privacy, security, governance, and ethics.

Lead AI platform architecture, design microservices and REST APIs, mentor engineers, and drive Agile practices for AI systems at scale.

Develop and deploy responsible AI solutions that identify, measure, and mitigate bias across Pinterest’s large-scale machine learning applications, including generative AI, search, and recommendation systems. Collaborate with trust and safety, user modeling, content understanding, and engineering teams to adopt fairness tooling and practices. Mentor engineers, contribute to technical strategy, and bridge advanced research with product impact.

Own and scale Collective’s data platform by building reliable batch and event-driven pipelines into BigQuery, modeling data with dbt, and enforcing quality, observability, security, and cost standards. Partner with engineers, analysts, and business stakeholders to deliver trustworthy datasets, metrics, reporting, and AI-enabled warehouse access. The role includes production ownership, incident response, governance, infrastructure practices, and support for analytics and LLM-based tools.

Architect and develop Java-based AI, IVR, conversational, NLP, and LLM solutions for high-volume customer interactions. Build classification and clustering models, analyze data with SQL, design RAG and prompt-engineering workflows, and lead agentic AI development. Establish LLMOps practices for monitoring, evaluation, and lifecycle management while partnering with platform, speech engineering, and product teams. Mentor junior and offshore engineers.

Build and integrate enterprise AI solutions using pre-trained LLMs, RAG architectures, traditional machine learning, AI agents, and deterministic logic. Develop backend APIs, React interfaces, data pipelines, and integrations with ServiceNow, SAP, Salesforce, and Azure. Support testing, deployment, monitoring, documentation, and workflow orchestration using LangChain and n8n while collaborating with AI, data, and platform teams.

Build, fine-tune, evaluate, and deploy production AI systems spanning vision, multimodal, generative, agentic, simulation, and robotics applications. Develop data and inference pipelines, optimize GPU workloads, monitor model performance, and improve robustness and safety. Collaborate with researchers, engineers, and customers to translate business needs into scalable AI solutions using NVIDIA, cloud, and MLOps technologies.

Build and improve production AI systems for audit and accounting workflows. Responsibilities include developing LLM-powered agents, retrieval and document intelligence systems, evaluations, monitoring, guardrails, human-in-the-loop review, and reliability infrastructure. The role partners with audit experts, product engineers, customers, and leadership to improve AI accuracy, latency, cost, security, and scalability while debugging failures in live workflows and shipping customer-focused features.

Own major technical initiatives from ambiguous customer and product problems through architecture, implementation, iteration, and production delivery. Build AI-native systems using LLMs, agents, evaluations, retrieval, enterprise context, and workflow automation. Set technical direction, make architectural tradeoffs, review work, unblock engineers, and translate customer signals into technical plans. Ship meaningful production work, clarify technical ownership, and reduce execution bottlenecks in a fast-moving startup environment.

The AI Engineer will develop AI/ML solutions for investment analysis, design applications, optimize models, and work with large datasets in healthcare and finance contexts.