Top Artificial Intelligence Jobs

Drive adoption of Stripe payment and monetization solutions for AI companies by architecting custom billing strategies, influencing product roadmap, engaging technical founders, supporting implementations, and leading cross-functional efforts to position Stripe as the AI monetization partner.

Lead AI-driven transformation across corporate finance by mapping workflows, building a reusable finance knowledge infrastructure, prioritizing automation opportunities, delivering 5–10 high-impact AI initiatives annually, and partnering with Engineering, Data Science, and global finance teams to embed scalable AI solutions and enable change management.

Design, build, and operate large-scale AI/HPC infrastructure spanning job scheduling (Beaker), runtimes, networking, and storage. Drive automation, performance optimization, root-cause analysis for distributed GPU workloads, mentor engineers, and collaborate with researchers to prioritize and deliver scalable orchestration and cluster health tooling.

Lead end-to-end solution design and reference architectures for Physical & Edge AI deployments (connectivity, edge/cloud compute, perception, safety). Create architectural artifacts, drive design standards, engage customers and partners from proof-of-concept to production, and ensure production-readiness and alignment with platform and business goals.

Design and own the platform architecture for T-Mobile's Physical & Edge AI developer surface (SDKs, app marketplace, workflow orchestration, billing/metering). Define and enforce API, identity/auth, multi-tenant, and observability standards; produce high-level designs and RFCs; translate business requirements into scalable solutions; lead technical reviews and partner integrations; and mentor architects and senior engineers.

Design, build, and productionize scalable AI systems for autonomous detection, RAG-backed investigations, and auto-remediation. Develop LLMs, graph-based reasoning engines, streaming pipelines, evaluation/red-team processes, and collaborate with cross-functional teams to translate attacker behavior into robust detections.

Design, develop, and productionize scalable AI systems for autonomous threat detection and response. Build LLM-based reasoning engines, graph-based analytics, streaming feature pipelines, orchestration frameworks, and evaluation/reliability tooling. Collaborate with product, detection engineering, and customer teams to translate attacker behavior into robust ML and rule-based detections.

Lead design and delivery of scalable AI systems for autonomous threat detection and response. Build and productionize LLMs, graph reasoning engines, streaming feature pipelines, and orchestration frameworks. Own evaluation, reliability, and cross-functional collaboration while mentoring engineers and experimenting with RAG, tool-calling agents, and multi-modal models.

Lead AI infrastructure projects, develop GenAI solutions, drive automation strategy, and collaborate with IT and business teams for implementation.

Design, build, and ship iOS and/or Android pet product features using AI-native workflows. Collaborate with cloud, design, and product teams, integrate event-driven data, ensure observability and quality, and contribute to CI/CD and engineering standards.

Lead AI architecture and technical strategy across the full lifecycle: design, build, and operate LLM-powered systems and scalable ML platforms. Implement event-driven, distributed systems, integrate AI with enterprise backends, ensure reliability/security/monitoring, and mentor engineers while shaping organizational AI standards and roadmaps.

Lead EE/SW integration and build end-to-end toolchains for vehicle software: CI/CD, ECU flashing, HIL/SIL test orchestration, telemetry ingestion, ML-driven test prioritization, anomaly detection, NLP log triage, and dashboards to gate releases and enable scalable vehicle validation workflows.

Eight-week mentor-guided weekend program that provides hands-on enterprise AI workflow and automation experience. Participants build agentic systems, RAG, API-integrated workflows, and portfolio projects using tools like n8n, OpenAI, Airtable, and PostgreSQL, with deliverables such as BRDs, workflow diagrams, and prompt libraries.

Eight-week remote weekend mentorship program providing hands-on exposure to AI workflow automation, agentic workflows, n8n, API integrations, prompt engineering, RAG systems, and capstone projects focused on enterprise automation and documentation practices.

Design and deliver LLM/GenAI solutions for web-based workflow applications: refine use cases, build POCs and production agentic systems, implement RAG and orchestration pipelines, integrate AI APIs, ensure observability and responsible-AI guardrails, maintain full-stack features, and participate in CI/CD and security compliance.

Partner with account executives to demonstrate Arize, advise on GenAI/ML best practices, discover prospect tech stacks, lead technical demos and PoCs, handle technical objections, and drive POCs from requirements to close.

Lead design and delivery of an AI agent platform: agent orchestration, tool pipelines, memory systems, and APIs. Integrate LLMs, embeddings, vector search, and RAG into scalable, observable services. Own end-to-end designs, code reviews, cross-functional collaboration, reliability improvements, and mentor junior engineers while ensuring security and platform standards.

As a Staff Software Engineer at Cloudera, you will design and implement scalable AI and machine learning applications, collaborate with cross-functional teams, and adhere to engineering best practices.

Drive adoption of AI Funds' ETF offerings by developing territory sales plans, building advisor and institutional relationships, delivering in-person presentations, supporting clients, collaborating with internal teams, attending industry events, and tracking CRM activity. Travel across North America and regular Chicago office presence required.

Design, build, and operate scalable data pipelines and platforms (Snowflake, Databricks, Delta Lake). Implement dbt models, semantic layers, data quality, orchestration (Airflow/Dagster/ADF), and DevOps for data. Build AI-enabled pipelines for RAG, embeddings, vector stores and integrate LLMs into ETL. Ensure reliability, monitoring, and cost-effective cloud architectures across AWS and Azure.

Own end-to-end QA for AI SMS bot and call-training system: review production conversations, create JSON test files, edit Markdown knowledge base (WRONG/RIGHT examples), run test suites, verify fixes, and maintain knowledge base. Operate existing infrastructure using CLI and Git, and write realistic conversational examples and pass/fail criteria.

Lead enterprise adoption and implementation of AI tools and LLMs, partner with business leaders to identify AI opportunities, design AI-driven process improvements, create SOPs and training, manage change, track adoption and impact, and ensure responsible, compliant AI usage.

Design, build, and deploy production AI agents and workflows for supplier management. Own the full AI stack (model selection, prompt engineering, RAG, tool use, evals) and integrate agentic systems with ERPs, communication platforms, and third-party APIs. Iterate for reliability, collaborate with product and domain experts, and shape applied AI architecture and methodology across the company.

Design, research, and deliver LLM and GenAI solutions for healthcare SaaS at scale: architect systems, handle healthcare data, implement PEFT and RAG pipelines, ensure responsible AI, mentor teams, and translate technical outcomes to business impact.

Produce end-to-end AI-assisted creative assets for brand and demand-generation campaigns across video, motion, static, and animation. Use AI generation tools alongside traditional editing to create ads, demo/explainer videos, and TV/streaming commercials; maintain brand fidelity, build prompt systems and reusable templates, iterate quickly based on feedback and performance, and collaborate with designers, writers, and demand generation partners.