Top Artificial Intelligence Jobs

Designs and implements architecture for AI agents within the Bluefield framework. The role develops technical blueprints, optimizes Google Agent Space and Google Cloud services, creates reusable agentic components, integrates agents with enterprise systems and APIs, leads prototypes, troubleshoots complex issues, documents architectures, conducts technical reviews, mentors Generative AI Engineers, and presents technical work internally and externally.

Participate in full-stack development and maintenance of Binance’s KYC system, including backend services, frontend features, documentation, testing, regression, troubleshooting, and customer issue resolution. The role is a 3–6 month accelerator internship for current university students and recent graduates, with an emphasis on Java, Spring Boot, databases, middleware, and frontend technologies.

Build and deploy AI systems for life sciences research institutions. Partner with scientific and engineering teams to integrate Claude into research workflows, develop MCP servers, benchmarks, agent skills, and domain-specific infrastructure, and identify deployment challenges involving heterogeneous data and auditability. Create documentation and technical content that enables partners to self-serve and scale successful solutions across the life sciences ecosystem.

Owns post-certification customer success for AIUC-1, helping customers use certification to accelerate enterprise sales, generate ROI stories, secure marketing placements, and drive renewals and upsells. Builds executive relationships, manages announcements and customer activations, supports sales calls and security reviews, develops enablement materials, and creates repeatable success processes. The role also channels customer feedback to Product and requires commercial ownership, cross-functional execution, and team leadership.

Own the technical direction and implementation of authentication, authorization, and on-behalf-of delegation for Redpanda’s agentic AI platform. Extend its OAuth authorization server and token vault, build auditable delegation systems, and federate with enterprise identity providers such as Okta, Entra, and Ping. Lead technical decisions across multiple engineering teams, track emerging agent identity standards, collaborate with security and product teams, mentor engineers, represent technical positions externally, and deliver production-hardened systems.

Own the architecture and implementation of Applied AI backend systems, including APIs, data models, distributed pipelines, orchestration, infrastructure, and production reliability. Lead technical direction, build high-throughput services, manage containerized infrastructure with Terraform, participate in on-call, resolve incidents, mentor senior engineers, and collaborate across product, operations, and research teams. The role requires strong backend, database, distributed systems, and production operations expertise, plus enthusiasm for agentic AI development.

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.

Lead development of AI-powered employee experiences on a web surface. Define product specifications, success metrics, roadmaps, and strategic bets; partner with ML, engineering, UX, data science, marketing, and customer teams; translate user feedback and ambiguous problems into scalable enterprise capabilities; and guide rollout strategies and feedback loops for products serving large user bases.

Lead end-to-end ownership of CMC data products and AI/ML solutions, including data governance, architecture, pipelines, semantic models, AI interfaces, validation frameworks, and production scaling. Partner with data engineering, analytics, IT, and senior stakeholders to move AI capabilities from MVP to enterprise use, ensure regulatory and privacy compliance, define technical standards, and drive adoption through compelling business communication.

Develop and prepare multi-dimensional semiconductor layouts from schematics, lead major block developments, verify data integrity, debug physical verification issues, mentor juniors, improve automation and layout methodologies, and coordinate with global design, verification, and CAD teams.

Lead strategy and roadmap for Lowe's Media Network adtech systems and platforms. Translate business requirements into scalable technical specifications, oversee integrations (CRM, OMS, DSPs), drive automation/AI adoption, ensure data quality and reporting, liaise with Product/Engineering/Marketing, and manage a small cross-functional team to enable seamless campaign execution and growth.

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.

Design and architect Android systems and modular components to support multiple apps. Ship end-to-end user features using an AI-assisted workflow, improve developer tooling and CI, set quality and safety standards for AI-generated code, solve performance and production issues, and document playbooks for the team.

Lead customer-facing solution sales for Verily’s precision health data platform. Responsibilities include product demonstrations, pilot support, strategic enterprise sales, value proposition development, contract negotiation, pricing, customer and partner feedback, and cross-functional collaboration with product, marketing, delivery, and account teams. The role focuses on growing business with life sciences customers in research data platforms, SaaS, genomics, and clinical research services.

Leads firmwide communications, change management, training, and adoption initiatives supporting AI and technology transformation. Develops campaigns, stakeholder engagement plans, training resources, and internal narratives; measures effectiveness and refines programs. Partners with senior leaders and cross-functional teams to build AI literacy, organizational readiness, and sustained adoption while mentoring junior staff.

Leads the engineering team developing Robinhood’s agentic AI platform and applications. Responsibilities include hiring and developing engineers, setting technical direction for orchestration, tool integration, evaluations, permissioning, guardrails, and approval flows. The role owns production reliability, SLOs, incident resolution, auditability, and measurable performance across large-scale nondeterministic systems, while partnering with Legal, Compliance, and Security in a regulated financial 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.

Provides architecture leadership for enterprise Data & Analytics initiatives. Defines end-to-end cloud and hybrid solution architectures, governs architecture reviews, modernizes data platforms, and designs secure, scalable solutions supporting analytics, reporting, AI/ML, and digital transformation. Partners with business and technical stakeholders to create roadmaps, implementation plans, architecture artifacts, and governance standards. Presents solutions to architecture review boards and guides data strategy, security, compliance, integration, and responsible AI practices.

Own the full B2B sales cycle for AI-trained talent placements, from warm executive introductions and consultative discovery through deal structure, closing, and successful hiring. Build and manage the sales funnel, forecast accurately, maintain pipeline discipline, and refine the placement motion based on market feedback. Develop commercial infrastructure, tailor talent solutions to employer needs, and use AI tools for research, personalization, follow-up, and workflow automation.

Build and execute AI-powered workflows for scripted content, leading projects from concept through delivery. Design scalable production pipelines, identify AI integration opportunities, collaborate with R&D and technical teams, and oversee field, post-production, and delivery operations. Manage cross-functional teams, vendors, and production partners while solving risks and adapting processes in a fast-moving environment.

Designs cache subsystems for Intel AI SoCs, including microarchitecture, RTL coding, integration, simulation, timing constraints, and verification. Evaluates power, performance, and area tradeoffs; supports synthesis, timing closure, formal equivalence, silicon bring-up, post-silicon validation, and debugging. Requires advanced Verilog/SystemVerilog expertise, SoC integration experience, bus protocol knowledge, scripting skills, and leadership in mentoring engineers and improving design methodologies.

Entry-level role supporting the evaluation, development, and rollout of generative AI solutions across business teams. Responsibilities include building prompts, datasets, documentation, prototypes, and AI-driven automations; testing and optimizing AI tools; researching emerging technologies; evaluating workflows; and supporting organizational adoption. The position involves close collaboration with product, engineering, and business stakeholders and offers hands-on learning in AI implementation.

Collaborate with product, engineering, and business teams to evaluate and implement generative AI solutions. Build prompts, datasets, documentation, prototypes, and automated workflows; test and optimize AI tools for accuracy and business relevance. Research emerging AI trends, assess platform capabilities, support production workflow evaluation, and help teams adopt AI solutions across the organization.