Top AI Engineer Jobs

As a Staff Software Engineer, you'll design, build, and test Gusto's core platform services, mentor engineers, and collaborate with various teams to solve complex engineering problems.

Design and build AI Gateway systems for high-volume, latency-sensitive inference at the edge. Implement APIs, routing, observability, controls, developer workflows and tooling. Prototype ambiguous ideas to production, improve reliability, performance, cost-efficiency and debuggability, and mentor engineers while dogfooding AI-native tools, agents, and automation to accelerate development and operations.

Lead design and delivery of enterprise integrations, automation, and AI-driven workflows across SaaS platforms. Architect scalable APIs, middleware, and conversational self-service experiences; partner with Security, Infrastructure, and Product; mentor engineers and drive long-term platform strategy, governance, and operational excellence.

Build and maintain production generative AI agents, LLM services, APIs, RAG pipelines, evaluation frameworks, and observability infrastructure. Develop intent recognition, query decomposition, context engineering, and self-service data capabilities. Integrate agents with internal and third-party systems, optimize latency and cost, automate business processes, and support AI-driven media planning and data monetization. Collaborate cross-functionally in an agile environment while adapting to evolving requirements.

Design and build end-to-end software primitives for AI infrastructure: domain modeling of power/datacenters/chips, authorization, audit, orchestration, vendor data ingestion (KMZ/BIM/Revit), and agent integration to enable safe, auditable agent operators. Ship production-quality systems and work across ambiguity to turn unstructured data into durable, composable entities.

Own the full product loop: identify high-impact problems, rapidly prototype and ship AI-accelerated solutions, build internal tools and automations, collaborate with operations and business teams, and iterate based on usage and data to drive adoption and business impact.

Design, build, operate, and maintain enterprise AI platform services and agent runtimes. Develop APIs, SDKs, MCP servers, tool interfaces, retrieval and embedding pipelines, model serving, evaluation infrastructure, and distributed data foundations. Establish engineering, security, testing, CI/CD, and infrastructure standards while optimizing platform cost and scale. Apply AI/ML capabilities to healthcare workflows, ensure PHI protection and tenant isolation, evaluate emerging technologies, and mentor engineers while influencing technical strategy.

Secure LLMs, generative AI systems, ML infrastructure, training pipelines, and AI applications. Responsibilities include threat modeling, architecture reviews, security controls, hardening, vulnerability remediation, data privacy, incident response runbooks, vendor reviews, governance, policy development, and AI security training. The role also supports proof-of-concept security solutions and monitors emerging AI threats.

Conduct AI security research, agentic and LLM testing, vulnerability discovery, adversary simulations, and full-chain red team operations. Test WAF, EDR, and SIEM detection efficacy using MITRE ATT&CK, collaborate with blue teams and incident response, develop detection validation frameworks, report risks to executives, and mentor security professionals.

Design and build security foundations for AI products, including agent runtimes, tool permissions, retrieval, content writes, observability, and auditability. Create reusable security libraries, guardrails, test fixtures, and automated red-team testing for prompt injection, data exfiltration, tool misuse, cross-tenant leakage, and unsafe mutations. Translate threat models into engineering requirements and launch criteria, and continuously harden systems using production signals and incident learnings.

Design and implement application security controls for AI applications, agentic systems, and Vertex AI platform services. Build guardrails against prompt injection, unsafe tool invocation, data exfiltration, and model abuse. Integrate security into CI/CD, software development lifecycles, infrastructure-as-code, and cloud environments. Partner with AI, MLOps, cloud engineering, data science, and model risk teams while developing Python automation, detection capabilities, and reusable security standards.

Designs and implements production machine-learning workflows for semantic search, classification, entity resolution, retrieval, embeddings, and RAG. Builds reliable AI services, integrates models with data and backend systems, evaluates mission performance, and supports deployment, monitoring, versioning, and MLOps practices. The role also contributes to architecture decisions and may support defense, intelligence, or mission-focused environments.

Lead strategy and technical execution of vulnerability management, offensive security, and AI-augmented defenses. Own full VM lifecycle, integrate scanning and asset systems, automate remediation, generate security insights, define KPIs/SLAs, advise executives, and mentor engineers to improve organizational security posture and compliance.

Designs, builds, and operates reusable enterprise AI platform services, backend APIs, model-serving capabilities, agent runtimes, retrieval services, and MLOps infrastructure. The role automates cloud-native deployments across hybrid and restricted environments using Kubernetes, CI/CD, and infrastructure-as-code. Responsibilities include implementing security, identity, observability, lifecycle management, scalability, resilience, and cost visibility for AI workloads while partnering with architecture, application engineering, cybersecurity, data, and product teams.

Develops and sustains cloud software supporting cyber intelligence and AI/ML capabilities on large-scale compute clusters and AWS infrastructure. Responsibilities include requirements analysis, software development, integration, testing, validation, installation, enhancement, maintenance, and issue resolution. The role works with distributed NoSQL systems, big-data technologies, cloud scalability, machine learning, large language models, RAG, agentic workflows, microservices, Kubernetes, observability, and infrastructure-as-code tools.

Design, train, deploy, and optimize deep learning and computer vision models for semiconductor inspection systems. Develop production-quality algorithms combining deep learning with physics-based modeling or signal processing, manage datasets and evaluation pipelines, and improve inference performance using GPU and high-performance computing. Collaborate across engineering teams and support deployments at customer sites, including occasional international travel.

Designs and implements AI and machine-learning capabilities for cyber and intelligence workflows. The role captures mission-user processes, develops LLM and agentic automations, integrates AI with APIs and enterprise data, and deploys reliable solutions in classified environments. Collaboration with analysts, operators, developers, and leadership is required. Responsibilities include workflow mapping, orchestration, production integration, security governance, and support for rapid national-security decision-making.

Design and implement large-scale, distributed AI-driven SD-WAN systems and real-time, fault-tolerant services. Drive features end-to-end from design through testing and deployment, improve scalability and operability, participate in code and design reviews, and collaborate with product and documentation teams.

Troubleshoot complex Linux, NVIDIA GPU, CUDA, Docker, virtualization, networking, hardware, and AI workload issues. Own escalated support tickets, assist clients and infrastructure suppliers, identify root causes, and collaborate with engineering teams. Build Python and Bash diagnostic tooling, create runbooks, improve monitoring and troubleshooting processes, and support supplier onboarding and machine management. The role is fully or mostly on-site in Los Angeles, with schedules covering Monday-Friday or Sunday-Thursday.

Design and deploy AI-enabled decision workflows for Department of Defense and federal customers. Translate operational challenges into data models, dashboards, AI agents, automated workflows, and predictive or prescriptive solutions. Engage stakeholders, analyze and troubleshoot data, advise end users, support adoption, and collaborate with developers to improve the platform. The role combines solution engineering, analytics, customer advisory, data integration, and AI workflow design.

Drives hands-on adoption of agentic AI development across engineering teams. Embeds with product teams, diagnoses tooling and workflow blockers, configures MCP and IDE integrations, evaluates AI tools, and develops reusable prompts, skills, and automation patterns. The role also shares best practices, informs programme strategy, supports training, and improves engineering productivity across diverse codebases and divisions.

Designs and deploys AI-enabled decision workflows for Department of Defense and federal customers. Responsibilities include configuring data models, dashboards, what-if scenarios, AI agents, and automated workflows; integrating and transforming data; troubleshooting customer issues; advising end users; supporting adoption; and collaborating with product and development teams. The role combines solutions engineering, analytics, customer advisory, and agentic AI workflow design across enterprise platforms.

Architect and build backend systems powering conversational AI experiences. Lead complex projects across the software development lifecycle, define technical direction, design scalable solutions, and ensure system quality. Mentor engineers through code reviews, knowledge sharing, and onboarding while collaborating cross-functionally on product strategy, experiments, and high-value initiatives. Develop LLM-driven infrastructure and maintain operational excellence across production systems.

Lead the architecture and implementation of scalable conversational AI systems and backend services. Own projects from ideation through execution, shape the engineering roadmap, design RESTful APIs and business logic, integrate external partners, evaluate ML-driven results, and optimize platform scalability. The role also includes technical leadership, mentoring, code reviews, cross-functional collaboration, and product-focused decision-making.

Build Clarity’s product experiences end to end, collaborating with product design to create an intuitive user experience. Develop frontend infrastructure, abstractions, design systems, performance and monitoring capabilities. Own product improvements and make decisions amid ambiguity. Support customers, participate in sales calls and onboarding, and explore generative AI applications that solve customer problems.