Top Artificial Intelligence Jobs

Designs and operates production multi-agent AI systems, LLM workflows, scoring and optimization algorithms, and closed-loop autonomous processes. Responsibilities include AI model evaluation, production monitoring, governance, third-party AI integration, federated data retrieval, and identifying patentable inventions. The role requires deep machine learning expertise, algorithm development, and deployment experience across complex operational environments.

Create daily 60–90-second vertical micro-drama episodes for TikTok, Instagram Reels, and YouTube Shorts using AI video, image, and audio tools. Generate scenes, edit footage in 9:16 format, maintain character consistency, synchronize music and sound effects, add captions and overlays, and optimize exports for social platforms. The role requires prompt engineering, visual composition, and editing skills, with traditional production and storytelling experience considered advantageous.

Build and scale LLM-based, agentic customer-engagement systems: architect, deploy, and monitor production ML features; finetune models; create evaluation frameworks, feedback loops and HITL workflows; run A/B tests; optimize latency, cost, and reliability; and mentor the team.

Build and improve an internal AI agent platform using LLM APIs, tool calling, structured outputs, SQL, Python execution, and advertising APIs. Manage context, memory, guardrails, reliability, and evaluation frameworks while expanding document and workflow automation. Collaborate with machine learning, product, and full-stack teams to deliver robust AI applications and productivity tools.

Build and operate large-scale data infrastructure for AI training and evaluation pipelines. Responsibilities include data ingestion, transformation, quality assurance, lineage, versioning, reproducibility, and high-throughput delivery across diverse modalities. The role requires operating petabyte-scale systems, applying distributed systems and data modeling expertise, supporting accelerator-based training, and following strong software engineering practices such as testing, CI/CD, and code review.

Principal Software Engineer serving as a technical authority for backend, data, and AI platform architecture. Responsibilities include designing large-scale distributed data platforms, streaming systems, APIs, data models, and production AI systems using LLMs, RAG, vector search, and agent orchestration. The role establishes architectural standards, governance, observability, security, and cost controls; contributes high-impact production code; guides modernization; partners with product and engineering leaders; and mentors senior engineers.

Design, optimize, and deploy machine learning models for resource-constrained edge devices, including mobile and embedded platforms. Apply model compression, quantization, pruning, hardware-aware optimization, performance profiling, and systems engineering to deliver reliable production AI outside the data center. Collaborate cross-functionally while addressing on-device privacy, security, compute, memory, energy, and connectivity constraints.

Designs, deploys, and operates secure cloud and hybrid infrastructure for enterprise AI workloads. Responsibilities include container orchestration, GPU-enabled compute, APIs, microservices, CI/CD, infrastructure-as-code, MLOps, model serving, monitoring, data platform integration, and security governance. The role partners with architecture, cybersecurity, data engineering, and application teams to enable AI solutions across corporate and manufacturing systems.

Evaluate AI coding agents such as OpenAI Codex and Claude Code for usefulness, correctness, engineering judgment, reasoning quality, and user trust. Provide rigorous, opinionated feedback, distinguish response-quality levels, and help define best practices for AI-assisted development interactions. The role requires senior engineering judgment rather than production coding, with a take-home evaluation and behavioral interview.

Own and engineer an enterprise AI collaboration platform spanning Slack, Google Workspace, Glean, Claude, and ChatGPT. Build production AI agents, RAG integrations, APIs, connectors, workflows, and secure enterprise integrations using MCP and function calling. Administer platform configurations, access controls, monitoring, testing, governance, and lifecycle management. Lead architecture reviews, troubleshoot complex platform issues, establish engineering standards, mentor contributors, and evaluate emerging AI capabilities.

Optimize throughput, latency, and cost across large-scale neural network training and inference workloads. Responsibilities include GPU kernel optimization, distributed training and inference tuning, memory and parallelism optimization, compiler-level improvements, profiling, measurement, debugging, model compression, and production-scale LLM performance engineering. The role also involves cross-functional collaboration, code and design reviews, technical mentorship, and shipping reliable production systems.

Own and engineer an enterprise AI collaboration platform spanning Slack, Google Workspace, Glean, Claude, ChatGPT, and related systems. Build production AI agents, integrations, APIs, connectors, workflows, and RAG solutions while enforcing security, permissions, governance, and reliability. Administer platform configurations, troubleshoot complex issues, evaluate emerging capabilities, establish engineering standards, lead technical reviews, mentor contributors, and provide senior technical guidance.

Research and develop advanced machine learning systems, evaluate current AI techniques, and transition prototypes into scalable, observable production pipelines. Responsibilities include training, fine-tuning, and evaluating deep learning models; applying distributed and mixed-precision training; collaborating across the full ML lifecycle; and shipping impactful AI solutions. The role also involves assessing research literature, communicating complex methods, and continuously improving deployed systems with appropriate safeguards.

Leads enterprise AI transformation by developing the AI roadmap, prioritizing investments, managing initiative portfolios, and delivering measurable business value. Partners with executives and cross-functional teams to redesign operations, establish governance, evaluate technologies and vendors, drive adoption, and lead workforce change. Accountabilities include revenue growth, productivity, efficiency, customer experience, compliance, AI maturity, and value realization.

The Conversion Engineering-Global Content Consultant role focuses on consulting Global Data Conversion Software Engineering, improving processes, and implementing AI solutions. Responsibilities include supporting offshore teams, driving project completion, and creating documentation for standards.

Lead design, experimentation, and validation of agentic AI capabilities for legal products using ML, NLP, LLMs, RAG, and hybrid retrieval. Define agentic workflows, retrieval strategies, evaluation metrics, and benchmarking. Analyze large legal datasets, translate results into product recommendations, collaborate with product/engineering/legal experts, and mentor the data science team.

Hire for two onsite roles: Content Moderator to review and enforce community safety guidelines, and Content Rating Specialist to test and grade LLM/AI outputs and annotate/extract data from long-form audio and text. Both require strict adherence to evolving client rubrics, high attention to detail, resilience handling sensitive content, and flexible shift availability.

Own and grow the Accenture alliance in North America by creating joint value propositions and driving software ARR and CoE adoption. Manage end-to-end sales engagements, partner business planning, co-marketing, and pipeline alignment while collaborating with product, pre-sales, delivery, and Accenture teams. Build executive relationships, sell complex/multimillion-dollar solutions, and productize repeatable IP. Maintain deep product and market knowledge and travel as needed.

Own the maintenance, security hardening, multi-tenant provisioning, deployment, and operation of an AWS-hosted AI SaaS platform. The role will also help commercialize the product through packaging, positioning, and go-to-market efforts. Applicants should have independently shipped and operated a real SaaS product and be comfortable working as the sole or near-sole engineer. Compensation is currently revenue share, equity, or another negotiated arrangement without guaranteed cash pay.

Lead design and scale-up of robust downstream purification processes for biologics across development stages. Drive process characterization, regulatory filings, and technology innovation (continuous biomanufacturing, PAT). Collaborate cross-functionally, publish and present results, mentor staff, and influence strategic initiatives while championing safety and compliance.

Lead complex data exploration and analytics projects for DoD clients using SQL, R, Python. Perform data cleaning, visualization, and analysis of structured and unstructured data. Develop predictive models, NLP/text-mining and machine learning solutions, advise stakeholders, and collaborate on publications and presentations.

Own and build Practice Flow’s B2B performance marketing engine for medical practices, beginning with Primary Care. Manage Google, Meta, and LinkedIn campaigns; develop ads, landing pages, lead magnets, webinars, and email nurture; instrument attribution; and optimize CPL and CAC through testing. The role is fully independent, funded by performance and recurring revenue share, and does not involve sales calls, patient information, or website management.

Support Phoenix office marketing, business development, and communications. Manage attorney relationships, events, proposals, directory submissions, content creation, sponsorships, and local market initiatives while coordinating with firmwide teams.

Field-based Territory Manager responsible for new business development, strategic selling to cardiology/EP stakeholders, full sales-cycle management, territory planning and pipeline forecasting, cross-functional account management, and meeting quarterly/annual sales targets while traveling extensively within a defined geographic territory.

Support day-to-day operations for a residential community by handling resident communications, vendor coordination, accounts payable tasks, office and front-desk management, leasing backups, event coordination, record keeping, and special projects to ensure consistent community management and exceptional resident experience.