Top Artificial Intelligence Jobs

Design, build, and deploy end-to-end ML and generative AI solutions for clients: data exploration, model training/evaluation, production deployment, MLOps, monitoring, and advisory. Apply LLMs (RAG, embeddings, fine-tuning), establish CI/CD and responsible-AI practices, present findings to stakeholders, and mentor teammates while contributing across engineering and consulting tasks.

Lead and drive design-phase execution for rack-scale AI platforms. Maintain master schedules, track critical-path items and risks, coordinate cross-functional design and readiness reviews, engage CM/JDM partners, ensure validation readiness, and provide executive updates on milestones, risks, and resource gaps.

Design, implement, and run automated benchmarking and validation pipelines for AI-driven genomic interpretation. Expand regression datasets, execute large-scale validation across curated VCFs, surface accuracy regressions, build automated QA for AI releases, and integrate validation into CI/CD with dashboards and documented failure modes.

Lead evaluation and benchmarking of AI-driven genomic interpretation: own gene/disease/variant mapping logic, define accuracy and release criteria, establish regression standards, partner on automated validation, and guide where deterministic logic supplements models.

Lead company-wide AI and emerging tech strategy for genomics products. Define roadmaps, build AI agent frameworks and MLOps infrastructure, ship AI features across web and native apps, drive adoption and metrics, and hire and mentor an AI/bioinformatics team while aligning cross-functionally with product, engineering, design, growth, and the CEO.

Lead and grow the mobile and AI platform engineering team to deliver reliable, real-time DNA-based health insights. Own roadmap, delivery predictability, React Native releases, performance, production reliability, AI model orchestration, multi-agent systems, incident response, API contracts, and cross-functional partnerships to ensure scalable, trustworthy AI experiences.

Own the long-term vision, roadmap, and execution of Sequencing's AI platform to turn genomic data into conversational, personalized health guidance. Lead AI-native product strategy, evaluate and productize emerging models and agents, partner with engineering and bioinformatics, run experiments and analytics, and optimize engagement, retention, and trust for mobile and web experiences.

Partner with the CEO/COO to translate strategy into execution, build AI-enabled operating systems, drive cross-functional programs, run OKRs and planning cadences, scale vendor/procurement, and lead high-priority projects end-to-end.

Design, build, and deploy agentic AI solutions (Google Gemini, Claude, ADK); maintain BigQuery data warehouse and ETL pipelines; troubleshoot tracking across Google Analytics, Omeda, Piano, and Salesforce; create dashboards and monitoring to ensure data quality and measure agentic impact across Sales, Marketing, Editorial, and Finance.

Design, implement, and optimize GPU kernels, kernel compiler, memory planner, and runtime for low-latency generative AI inference. Analyze performance bottlenecks across hardware and software, collaborate with infrastructure teams, and maintain production profiling, benchmarking, and validation tooling while supporting new model architectures and multi-GPU strategies.

Design, deploy, and operate large-scale LLM and multimodal inference architectures. Work hands-on with customer engineering teams to containerize, scale, monitor, and troubleshoot GPU-based inference workloads across Kubernetes, CI/CD, and hybrid/on-prem environments. Create Helm charts, Terraform modules, and observability tooling while delivering workshops and platform reliability insights.

Design, build, and maintain agent APIs and production agent applications for document understanding, advanced RAG, and customer support automation. Integrate open-source models, collaborate with backend and infra for deployment and monitoring, and ensure APIs are robust, scalable, and developer-friendly.

Lead design and scaling of AI-powered backend systems for document processing, entity resolution, extraction agents, and conversational interfaces. Build resilient pipelines, orchestration, observability, and evaluation tooling; make architecture decisions, mentor engineers, and partner with product to deliver production-ready applied AI workflows.

Build and operate backend AI systems: document processing pipelines, extraction agents, entity resolution, computed attributes, and conversational query interfaces. Design reliable, observable infrastructure (queues, pipelines, orchestration) at scale, optimize prompt chains and retrieval strategies, and collaborate with product and frontend teams to expose AI capabilities.

Build and productionize AI applications integrating LLMs and vision models into an edge-to-cloud robotics platform. Fine-tune and quantize models, deploy to NVIDIA Orin/Jetson and cloud GPUs, integrate Vector DBs/RAG/agent frameworks, and create data pipelines to collect and version sensor data for retraining.

Design, build, and deploy AI-powered web applications end-to-end. Collaborate with customers to define product specs, implement front-end and back-end systems, build scalable data pipelines, integrate sensor data, optimize performance, mentor junior engineers, and maintain production deployments across government and industrial clients.

PhD-level AI Engineering intern to apply machine learning to large-scale intraday and tick financial data. Responsibilities include alpha generation, portfolio optimization, risk modeling, data preprocessing, and collaboration with researchers to deploy robust ML models for trading.

Lead and grow Matter's AI and data science efforts to build personalized meal-planning models. Develop ML and reinforcement learning algorithms, integrate LLM conversational interfaces, design feedback loops and dashboards, collaborate cross-functionally, and ensure alignment with nutrition science and ethical AI practices.

Lead product strategy for AI Personalization, developing AI systems for personalized customer support at Airbnb. Manage the full-stack systems, influence roadmap decisions, and collaborate across teams to optimize performance and user experience.

Own an AI-focused product area end-to-end: set vision and roadmap, partner with engineering/design/GTM, translate customer pain points into requirements, define success metrics, iterate using data, and drive adoption through close customer collaboration and cross-functional alignment.

The Product Analyst will enhance transaction enrichment products by analyzing data, defining requirements, and making recommendations to improve user experience and product performance.

Drive the development of an AI-powered SRE agent, focusing on reasoning, learning, integrations, and technical architecture while ensuring engineering standards.

The Senior AI Platform Engineer will architect and maintain Azure landing zones, design CI/CD pipelines, enforce security, provide mentorship, and drive incident response within AI workflows.

The Solutions Architect leads sales engineering efforts, engages clients, shapes strategies for data and AI solutions, and fosters collaboration across departments.

The Head of AI Success will drive customer retention, satisfaction, and revenue by building scalable systems, utilizing AI automation, and leading deployment and support teams.