Top AI Engineer Jobs

Designs, deploys, and operates production machine learning systems for identity resolution, scoring, deliverability, and personalization. Responsibilities include feature engineering, model training, real-time inference, graph database integration, monitoring, drift detection, retraining, low-latency APIs, and scalable data processing. The role also contributes to MLOps standards, GPU acceleration, and ML architecture while partnering with platform engineering.

Build and deploy production AI and process automation solutions for Corporate Services. Responsibilities include developing agentic workflows, LLM-powered analytics, document intelligence pipelines, RAG systems, copilots, and end-to-end workflow automation. The role partners with business stakeholders to scope, build, deploy, monitor, and iterate products while applying strong engineering practices, reusable patterns, testing, observability, security, and enterprise deployment standards.

Secure and operate AI platforms, gateways, agents, and tool integrations across their lifecycle. Build security automation, privileged access controls, network and endpoint protections, logging and telemetry pipelines, SIEM integrations, dashboards, alerts, and monitoring. Review integrations for security risks, enforce authentication, authorization, guardrails, and usage governance, and support firewall, SASE/ZTNA, WAF, identity, and infrastructure security engineering.

Build the end-to-end architecture of an AI-powered software factory, including high-throughput telemetry capture, privacy pipelines, knowledge graphs, autonomous coding agents, and production deployment. Personally execute client workflow mapping and software delivery, then automate those processes for future deployments. The role requires strong product judgment, full-stack ownership, enterprise workflow optimization, and in-person collaboration in San Francisco.

Own end-to-end analytics engineering for AI and developer-experience telemetry, including instrumentation, production pipelines, governed metrics, monitoring, executive dashboards, and analysis. Define and validate new measures of AI productivity and ROI, communicate findings to senior stakeholders, and recommend improvements to tooling, processes, and investment. Collaborate with data engineering, applied AI, and data science teams while using AI coding agents to build and maintain pipelines and reporting.

Designs and leads enterprise AI infrastructure, including large-scale GPU clusters, Kubernetes and HPC platforms, high-speed network fabrics, infrastructure-as-code, GitOps, workload scheduling, observability, and SRE automation. The role establishes architecture, optimizes systems performance, leads incident response and recovery, manages platform governance, and mentors engineers while collaborating with AI researchers, hardware teams, and technology partners.

Identify bottlenecks in professional creative workflows and turn them into AI-powered, end-to-end workflows and polished interactive prototypes. Research user needs, design intuitive experiences, define quality standards, validate hypotheses, and collaborate with product, model, and engineering teams to ship successful concepts. The role requires professional creative production knowledge, strong product taste, AI coding and prototyping ability, and experience using tools such as Figma, model APIs, and agent frameworks.

Build AI-powered features and products for clinical documentation and medical coding. Develop LLM-driven agentic systems, APIs, services, and production infrastructure across healthcare revenue cycle workflows. Create evaluation frameworks and experimentation loops using statistical and data-driven methods to ensure reliability, auditability, and alignment with coding guidelines and user feedback. Collaborate with product, design, machine learning, clinical, and revenue cycle experts to deliver high-impact SaaS solutions.

Lead infrastructure engineering for GenAI-powered agents, AWS cloud cost optimization, and FinOps automation. Architect runbooks, Policy as Code, and resilient infrastructure solutions while guiding teams on cost-effective design. Own FinOps AI applications, dashboards, documentation, compliance, and auditability. Collaborate across platforms to modernize technology processes, validate AI recommendations, and improve cloud efficiency, performance, security, and governance.

Own AI and developer-experience analytics end to end, including telemetry instrumentation, production data pipelines, metric governance, executive dashboards, and analysis. Define trustworthy metrics from evolving signals, monitor pipeline quality, communicate insights to senior stakeholders, and recommend actions to improve AI ROI and developer productivity. Use AI coding agents to build and maintain pipelines, dashboards, and analyses while partnering with data engineering, applied AI, and data science teams.

Build and deploy production machine learning and computer vision systems for edge devices. Responsibilities include data preparation, model selection and optimization, inference pipeline development, MLOps infrastructure, monitoring, customer solution delivery, and mentoring. The role emphasizes latency, throughput, accuracy, cost, and reliability tradeoffs for physical AI applications.

Leads full-stack software engineering initiatives using cloud technologies, distributed microservices, databases, APIs, and modern programming languages. Provides technical direction across Agile teams, mentors engineers, partners with architects and product managers, establishes engineering standards, improves system performance, reduces technical debt, and evaluates emerging technologies including AI-based development tools.

Lead full-stack software engineering initiatives and developer teams building distributed, cloud-native systems and developer experience platforms. Design architectures, implement high-reliability services, shape technology roadmaps, mentor engineers, and collaborate with product and architecture leaders. Work with Java, Python, front-end frameworks, APIs, databases, Docker, Kubernetes, cloud platforms, CI/CD, infrastructure as code, automated testing, observability, and AI-based development tools.

Leads full-stack software engineering teams and a portfolio of technology projects focused on developer experience, distributed systems, microservices, and cloud-native platforms. Sets technical strategy, oversees large-scale system design and delivery, manages engineering leaders, develops talent and succession plans, and aligns roadmaps with enterprise goals. The role involves Java, Python, cloud platforms, databases, containers, APIs, Agile delivery, and emerging AI technologies.

Build and lead full-stack software engineering projects using Java, Python, cloud platforms, microservices, databases, containers, and AI-enabled development tools. Provide technical leadership across Agile teams, define service architectures and integration patterns, improve system performance, reduce technical debt, establish engineering standards, mentor developers, and deliver secure cloud-based solutions supporting enterprise and regulatory needs.

Leads enterprise technology transformation, modernization, cloud adoption, architecture standards, engineering enablement, and responsible AI adoption. Develops modernization roadmaps, guides future-state architectures, promotes DevSecOps and platform engineering, evaluates emerging technologies, establishes governance and reusable patterns, mentors technical leaders, and influences technology strategy across engineering, architecture, security, infrastructure, product, and delivery organizations.

Develops scalable automation, AI, machine-learning, and data-analysis solutions for quantum-device research. Responsibilities include building experimental data pipelines, applying AI to device characterization and tuning, integrating models into simulation and control environments, creating reproducible workflows, and communicating technical solutions. The role collaborates across quantum research, engineering, software, and AI disciplines, with opportunities to build AI agents and copilots for experiment setup, log triage, reporting, and knowledge retrieval.

Build and deliver AI-powered healthcare products for patient engagement, care coordination, claims management, and operational efficiency. Develop full-stack applications, APIs, backend services, automation pipelines, and LLM integrations using Claude Code. Apply software engineering, DevOps, and MLOps practices for testing, deployment, monitoring, security, and performance. Collaborate with product, design, engineering, and clinical stakeholders in a remote Agile environment.

Lead the design and delivery of production-grade AI agents and automation workflows. Translate ambiguous business processes into reliable software, integrate agents with systems of record using MCP, define architecture and evaluation standards, and implement controls for permissions, observability, auditability, human review, failure containment, and rollback. Partner with business, data, and engineering teams, establish reusable patterns, mentor engineers, and guide responsible AI deployment.

Lead development of production-grade AI systems and intelligent agents for secure public-sector supply chain workflows. Design orchestration, planning, evaluation, observability, safety, and reliability capabilities for autonomous multi-step tasks. Establish technical strategy, architecture, and engineering practices while managing security, compliance, scale, availability, cost, and model behavior. Collaborate with product, engineering, customer-facing, and executive stakeholders, mentor engineers, and contribute to hiring and organizational growth.

Build and operate production private AI platform capabilities (agentic/multi-agent systems, orchestration, auto fine-tuning, runtime/model optimization). Implement evaluation, monitoring, safety/rollback controls, developer-facing APIs, and tooling. Partner with data and infra teams to ensure reliable, performant AI inference in constrained edge environments.

Lead design and development of enterprise software solutions using Java and Cursor AI. Provide technical leadership, mentor engineers, define architecture and roadmaps, implement cloud-native, microservices, CI/CD and infrastructure-as-code practices, and collaborate across product and engineering teams to deliver performant, secure systems.

Develop and test artificial intelligence and large language model systems. The provided description contains no additional responsibilities or requirements.

Build and ship AI-powered internal systems across investing, talent, founder services, finance, and operations. Projects include talent intelligence, sourcing and research tools, diligence workflows, firm knowledge search, operations automation, and reusable portfolio-company AI tools. The role requires end-to-end ownership, from identifying high-leverage problems and selecting the technical stack to building usable products, working with messy business data, and supporting portfolio founders with AI strategy and implementation.

Lead public cloud platform engineering for AWS environments, guiding application teams through cloud adoption, production support, reliability improvements, incident resolution, automation, and performance optimization. Build resilient, secure platforms using AWS services, containers, Kubernetes, infrastructure-as-code, monitoring, and DevOps tools. Evaluate production readiness through resiliency testing and chaos engineering while collaborating cross-functionally and applying AI/LLM tools to diagnose infrastructure issues.