Top Artificial Intelligence Jobs

Triage and resolve product bugs, interpret user feedback, monitor product quality, test fixes and edge cases, and document recurring issues. The role requires working directly in the codebase, using Claude Code and other AI tools, communicating clearly with teammates, and independently prioritizing issues that affect user workflows. This fully remote contractor position is open to candidates in the US or Canada, with potential for full-time conversion.

Own product quality by triaging user-reported bugs, interpreting feedback, submitting code fixes, testing edge cases, validating workflows, monitoring systemic issues, documenting resolutions, and communicating status across teams. The role requires independent judgment, strong product instincts, clear written communication, and practical use of Claude Code and other AI tools. It is a fully remote contractor role for candidates in the US or Canada, with potential conversion to full-time employment.

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.

Leads independent second-line cybersecurity risk oversight, including governance, risk assessments, risk appetite, KRIs, issue remediation, regulatory engagement, emerging technology risk, and executive reporting. The role challenges cybersecurity strategies and controls, oversees AI and quantum computing risks, supports audits and regulatory examinations, advises senior leadership and boards, and manages a high-performing cybersecurity risk team.

Build and lead production AI infrastructure for institutional commercial real estate. Develop full-stack workflows, document parsers, data foundations, MCP servers, LLM features, evaluation systems, and React-based portals. Deploy solutions in client Azure environments, productize reusable components, ensure auditable accuracy, work directly with clients, and help hire and lead distributed engineers.

Lead end-to-end performance optimization for LLM and VLM inference on NVIDIA GPU systems. Analyze workloads, build performance models, profile systems, optimize CUDA kernels and serving runtimes, and tune batching, caching, quantization, speculative decoding, and parallelism. Establish reproducible benchmarks and regression gates while collaborating across model, framework, networking, and GPU architecture teams. Contribute improvements to open-source inference projects including TensorRT-LLM, vLLM, and SGLang.

Own the full-cycle SaaS sales process for mid-market, multi-location, and franchise brands, from prospecting and discovery through demonstrations, negotiation, and close. Meet sales quotas, maintain accurate HubSpot forecasts, generate self-sourced opportunities, and communicate marketing technology value and ROI to senior stakeholders. Collaborate with SDR, Customer Success, Product, and Marketing teams to support pipeline development, client handoff, and market feedback.

Leads accounting systems transformation and finance automation, including NetSuite and Salesforce optimization, AI agent deployment, integrations, reporting, and internal tooling. Owns project plans, testing, training, go-live, controls, and executive reporting while partnering with engineering and cross-functional teams. Identifies automation opportunities, pilots AI vendors, supports SOX requirements, and trains finance staff on new workflows.

Deploys and configures AI capabilities across Workday and SAP Ariba, builds custom AI agents and automation, and establishes cloud PaaS foundations. Leads AI projects from concept through production, develops deployment pipelines, integrates enterprise systems, evaluates vendor and custom solutions, supports governance and security, and promotes AI literacy. The role requires extensive software engineering experience, enterprise cloud platforms, production AI/ML deployments, Python, SQL, APIs, and DevOps practices.

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.

Build and ship practical AI-powered tools, dashboards, bots, automations, and workflow integrations using Claude and Gemini APIs. Translate operational problems into scoped solutions, connect AI to business systems, iterate based on user feedback, and document every tool with a runbook. Responsibilities include agent and workflow design, MCP connectors, prompt evaluation, regression testing, API cost management, custom coding, and training internal users.

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.

Leads AI data engineering and actuarial analytics delivery, including Snowflake data products, pipelines, semantic layers, AI agents, dashboards, governance, and quality frameworks. Manages a complex technical team, modernizes legacy data ecosystems, establishes architecture and engineering standards, evaluates emerging AI technologies, manages portfolio budgets, and partners with actuaries, data scientists, IT, architecture, and business stakeholders to deliver production-ready analytics and AI solutions.

Provides full-time remote executive assistance to U.S.-based healthcare practice owners. Responsibilities include calendar and inbox management, task tracking, meeting coordination, reporting, patient and referral follow-up, vendor and invoice support, SOP documentation, and compliance reminders. Uses Claude AI to draft communications, summaries, agendas, and reports, while reviewing all outputs for accuracy, judgment, confidentiality, and HIPAA compliance.

Provides full-time remote executive and administrative support to healthcare practice owners. Responsibilities include calendar and inbox management, task tracking, meeting preparation, reporting, patient and referral follow-up, vendor coordination, invoicing, SOP maintenance, and compliance reminders. The assistant uses Claude to draft communications, summaries, agendas, and reports while reviewing all outputs for accuracy, confidentiality, and HIPAA compliance.

Lead research, development, and deployment of large-scale multimodal foundation models integrating wearable, language, biomarker, clinical, and behavioral data. Develop self-supervised and representation-learning methods, distributed training pipelines, and post-training techniques. Collaborate with engineering, MLOps, data, product, and research teams to operationalize robust, observable, privacy-preserving models and translate capabilities into member-facing features. Contribute to technical roadmaps and architectural decisions for WHOOP’s foundation AI systems.

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.

Leads and manages an AI Quality engineering team responsible for evaluating and improving AI outputs. Defines testing frameworks for model performance and drift, establishes technical strategy and architecture, provides technical direction, manages technology vendors and partnerships, drives engineering quality improvements, and mentors team members.

Lead governance, risk, and compliance activities for AI/ML systems, LLM integrations, AI agents, and related vendors. Conduct AI risk and third-party assessments, map controls to frameworks, manage remediation, support audits, develop AI-specific policies, report risk metrics, monitor regulatory developments, and coordinate incident governance with Legal, Security, Product, and Engineering teams.

Leads end-to-end design of AI-first consumer product experiences, from research and problem framing through prototypes and polished interfaces. Translates complex AI insights into intuitive, trustworthy workflows; conducts user research and usability testing; and collaborates with product, engineering, and data science teams on production-ready solutions. Advocates for accessible, inclusive, ethical, and transparent AI design while using Figma and AI-enabled tools throughout the design process.

Lead the technical direction and execution of WHOOP’s foundation AI modeling efforts across wearable sensor data, language, biomarkers, and behavioral signals. Drive high-risk research from hypothesis to production, shape AI strategy and infrastructure decisions, collaborate across research, product, and engineering, mentor senior technical talent, establish evaluation standards, and represent WHOOP externally through recruiting, partnerships, and the machine learning research community.

Build and own Python-based performance and power modelling frameworks for photonic AI hardware. Model concurrency, event-driven behavior, compute, memory, interconnects, and accelerator blocks; characterize AI workloads against hardware and RTL results; consume MLIR and compiler outputs; analyze simulation traces and benchmarks; validate power models; maintain CI/CD and simulation flows; and guide architecture and compiler decisions while developing future team members.

Leads the enterprise AI Engineering function, defining strategy, architecture, operating models, and engineering standards for AI agents and machine learning systems. Builds and manages senior engineering teams, develops secure cloud and on-premises AI infrastructure, oversees LLM deployment and MLOps practices, and partners with business, data, IT, and security leaders to deliver production AI solutions. The role also manages GPU procurement, vendors, capacity planning, governance, monitoring, and executive reporting.

Build and ship production AI-powered development features, including agentic workflows, LLM pipelines, retrieval systems, evaluations, fine-tuning, and latency optimization. Own AI feature quality from prototyping through deployment and monitoring. Collaborate with Product, Infrastructure, and Research teams to scale AI capabilities for Bubble’s visual app-building platform.

Develop and support scalable software and AI-powered applications for Bank of America’s conversational AI, search, and virtual assistant platforms. Responsibilities include designing architecture, coding, testing, building RESTful APIs and microservices, implementing Generative AI and RAG solutions, automating CI/CD and releases, troubleshooting production issues, and supporting deployment and monitoring. The engineer will collaborate in Agile teams, contribute to technical decisions, conduct code reviews, and mentor other engineers.