Top AI Engineer Jobs

Own the architecture and technical strategy for Square’s go-to-market systems. Build and operate production AI agents, automated workflows, and integrations that improve sales productivity. Lead complex initiatives through architecture, implementation, testing, deployment, and operations while establishing AI evaluation, observability, security, reliability, and human-oversight practices. Partner with Sales, Product, Data, and Engineering leaders to translate business needs into scalable solutions, make build-versus-buy decisions, mentor engineers, and improve business impact through data and user feedback.

Lead development of Avride’s ML infrastructure, initially owning GPU inference performance for onboard and simulation workloads. Build and optimize C++ execution frameworks, runtimes, and distributed infrastructure supporting neural network pipelines. Collaborate with applied ML teams on model architecture, diagnose performance issues, and expand ownership across broader ML pipelines. The role requires deep GPU and PyTorch knowledge, distributed systems experience, and strong expertise in multithreaded C++ environments.

Build and ship AI/ML products for finance and accounting workflows. Responsibilities include transforming messy financial data into production systems, designing evaluations, analyzing errors, optimizing quality and cost, and partnering with product and engineering. The role requires practical experience with machine learning, LLM applications, retrieval, embeddings, agentic systems, noisy data, and end-to-end production deployment.

Advise law firms on AI adoption across billing and revenue workflows. Configure Intapp Celeste AI agents, coworkers, and playbooks around billing guidelines and client terms; map work-to-cash processes; identify automation opportunities and risks; train billing and finance teams; drive adoption; and provide product strategy feedback. The role requires law firm billing or revenue operations experience, practical AI and prompt engineering fluency, strong stakeholder communication, and client-facing judgment.

Designs and implements AI-enabled business process transformations for clients. Responsibilities include prompt and context engineering, MCP tool and API integrations, agentic workflow design, knowledge architecture, documentation, evaluation, testing, governance, and human-in-the-loop controls. The role requires translating ambiguous business challenges into reliable AI systems, collaborating with executives and technical teams, and developing scalable operating models using modern AI tools.

Build and deploy production features for Marvell’s agentic AI software development lifecycle platform. Responsibilities include developing MCP integrations with engineering tools, automating CI/CD workflows, deploying containerized services on Kubernetes and AWS, writing production-quality code, measuring business impact, and collaborating with engineering and IT partners. The intern will work with LLMs, AI-assisted development tools, real users, and senior engineers.

Design and integrate production AI/ML systems for USCYBERCOM, including LLM agents, orchestration workflows, RAG pipelines, vector databases, MCP tool integrations, model routing, evaluation pipelines, and governance guardrails. Translate research prototypes into secure, enterprise-scale mission capabilities while benchmarking models, optimizing performance, and supporting rapid operational integration.

Leads software development and coaches engineers while driving adoption of AI-assisted coding, agentic workflows, prompt and context engineering, and AI governance. Reviews architecture, requirements, code, testing, security, resiliency, and production changes. Establishes controls for AI-generated output in a regulated banking environment, maintains shared AI development assets, improves engineering productivity, and communicates technical progress and risks to stakeholders.

Own the architecture and technical direction of Square’s go-to-market systems. Build and operate production AI agents, automated workflows, and integrations that improve sales productivity. Lead complex technical initiatives, establish reliable and secure AI evaluation practices, make build-versus-buy decisions, and partner with Sales, Product, Data, and Engineering. Remain hands-on across design, implementation, testing, deployment, and operations while mentoring engineers and influencing senior stakeholders.

Own the architecture and technical strategy for Square’s go-to-market technology stack. Build and operate production AI agents, automated workflows, and integrations that improve sales productivity. Lead complex technical initiatives, establish AI reliability and security guardrails, define evaluation metrics, and guide build-versus-buy decisions. Partner with Sales, Product, Data, and Engineering leaders while mentoring engineers and remaining hands-on across design, implementation, testing, deployment, and operations.

Build and operate backend services for Mercor’s Applied AI platform, including APIs, data models, background jobs, and high-throughput pipelines. Own features from design through production, with responsibility for reliability, observability, performance, infrastructure, containerized environments, and incident response. Collaborate with product, operations, and research teams to turn ambiguous requirements into production systems.

Develop advanced personalization and recommendation systems for digital grocery shopping. Build, deploy, monitor, and maintain machine learning pipelines and microservice APIs supporting production inference. Apply natural language processing, generative AI, large language models, agentic AI, recommendation models, deep learning, transformers, and classical machine learning techniques to real-world business problems. Evaluate and tune models, communicate technical results, and contribute to scalable AI solutions.

Designs and leads production-grade test automation for complex digital and mixed-signal engineering and production environments. Builds CI/CD pipelines, hardware and instrument integrations, AI-assisted workflows, validation gates, observability, diagnostics, recovery processes, and reproducible containerized execution environments. Establishes reliable release criteria and mentors engineers while ensuring automation produces traceable, evidence-based results.

Deploys and optimizes semiconductor EDA-to-ATE conversion flows using Velocity and related tools. Responsibilities include developing STIL, WGL, EVCD, IJTAG, and SSN conversion methodologies; supporting Advantest platforms; debugging timing, protocol, and pattern issues; creating Python and Java automation utilities; applying AI-assisted diagnostics; collaborating with customer and internal engineering teams; and delivering training and technical workshops.

Build customer-facing agentic AI experiences, including chatbots, embedded assistance, MCP integrations, and automated reconciliation workflows. Partner with product teams, domain experts, customers, and engineers to identify valuable AI applications across fund-management software. Design reliable multi-step agent systems, evaluate model outputs and failure modes, and ship production software while shaping product direction in a fast-moving environment.

Leads the end-to-end architecture, deployment, operation, and scaling of AI compute infrastructure for orbital data centers. Owns GPU/CPU, networking, storage, orchestration, cloud-platform, spacecraft network, testing, and qualification strategies. Builds and manages the compute payload engineering organization, leads hardware/software co-design, and makes supplier, component, technology-partner, and make-or-buy decisions. Requires extensive hyperscale, HPC, AI infrastructure, GPU cluster, hardware, networking, storage, and team leadership experience.

Design and implement machine learning research systems for generative models, causal inference, planning, and multi-objective optimization. Build training and inference pipelines, dataset loaders, experiment tracking, versioned datasets, and containerized model components. Develop synthetic data campaigns, translate research prototypes into tested production code, present findings, document results, and collaborate with RF, DSP, formal methods, academic, and government stakeholders.

Design, develop, deploy, and maintain AI/ML models and pipelines. Collaborate with cross-functional teams, implement MLOps and CI/CD, monitor model performance, investigate incidents, ensure compliance with security/privacy/ethical requirements, and document model architectures and procedures.

Provides senior systems engineering leadership for integrating AI/ML capabilities into MQ-9 unmanned aviation systems. Develops requirements, architectures, implementation strategies, roadmaps, trade studies, technical evaluations, and engineering analyses. Oversees AI/ML data management, model validation, integration, testing, and deployment while coordinating government, industry, and engineering stakeholders. Supports defense acquisition, fielding, sustainment, deficiency investigations, and executive-level technical decision-making within the NAVAIR program environment.

Develop and deploy AI agents using Gracenote MCP servers, lead technical pre-sales and proofs of concept, architect enterprise integrations, guide customer implementations, and advocate for product improvements. The role requires translating complex AI architectures into business solutions, supporting API integrations, and engaging stakeholders from executives to developers. Travel to customer sites globally may reach 40%.

Design, develop, test, evaluate, deploy, and maintain AI/ML models across generative AI, classification, prediction, computer vision, NLP, and reinforcement learning. Process structured and unstructured data, build ETL pipelines, conduct experiments, measure performance, deploy models in cloud environments, document findings, brief stakeholders, educate staff, and provide AI/ML subject matter expertise.

Build and own a real-time conversational voice AI platform for automotive customers. Responsibilities include developing production systems across speech, LLM, telephony, and event-driven AWS services; optimizing latency and reliability; implementing observability, failover, infrastructure as code, CI/CD, and integrations; and leading technical decisions, engineering standards, knowledge sharing, and mentoring.

Build and own Android growth and monetization experiments across subscriptions, advertising, purchases, and upgrade flows. Partner with product, design, data science, and analytics to instrument experiments, evaluate results, and ship reliable experiences at consumer scale. Use AI-native workflows for specification, coding, testing, and review; improve mobile engineering practices; contribute to architecture and code reviews; participate in on-call; and mentor engineers.

Design, build, deploy, and support enterprise AI agents, copilots, intelligent workflows, and lightweight applications. Integrate AI solutions with APIs, Microsoft 365, SharePoint, CRM systems, and business data while ensuring security and scalability. Translate business needs into practical solutions, prototype emerging technologies, improve agent performance, and collaborate with stakeholders to drive user adoption and measurable business value.

Build and maintain backend infrastructure and member-facing generative AI features that deliver personalized health and performance insights. Collaborate with Product, Design, Analytics, and Data Science teams to define requirements and deliver scalable solutions. Develop reliable software using Java, Kafka, AWS, and PostgreSQL; design RESTful APIs and relational databases; optimize performance; and maintain observability and monitoring systems.