Top Artificial Intelligence Jobs

Serve as a Laudio platform expert and hands-on trainer/coach to drive user adoption, onboarding, and satisfaction. Own utilization metrics for a client portfolio, develop outreach strategies, create training content and materials, escalate user feedback to Product, and partner cross-functionally to optimize processes and demonstrate platform value. Travel to support primarily New York clients.

Advise enterprise customers on cloud and AI efficiency, cost optimization, infrastructure remediation, and agentic workflows. Own a portfolio of NAM accounts, deliver strategic engagements, and build PointFive’s professional services offering from the ground up. Partner with Sales, Product, and Engineering to support deals, renewals, roadmap development, and scalable automation. Establish best practices for AI and token spend management while serving as a thought leader through writing, speaking, and industry participation.

Account Executives at LinkSquares manage the entire sales process, focusing on new customer acquisition through research, outreach, and closing. They prioritize teamwork, quotas, and deadlines while driving business opportunities in a collaborative culture.

The Product Marketing Lead will own product launches, develop messaging and campaigns, and lead cross-functional teams while driving product adoption and revenue.

This role involves leading engineering teams, owning technical direction, and managing release cycles for a gaming platform, with hands-on coding and architectural responsibilities.

Provides practical legal advice on domestic and international employment matters, including employee relations, compliance, investigations, accommodations, wage and hour issues, terminations, agreements, policies, claims, audits, litigation, and immigration initiatives. Partners with HR, leadership, and cross-functional teams to manage risk and support business growth.

Build and lead production AI engineering solutions for client engagements, including agentic systems, RAG pipelines, vector retrieval, MCP integrations, evaluations, observability, and guardrails. Partner with architects and consultants, coach client engineering teams, and take AI systems from prototype through monitored production deployment. The role also contributes to internal AI products and reference architectures.

Develop and validate SystemC/TLM performance models for AI silicon subsystems, including SoC fabrics, memory systems, interconnects, and data movement paths. Analyze workloads, scalability, bandwidth, latency, congestion, and bottlenecks; correlate models with simulations and hardware measurements; build Python-based analytics, automation, dashboards, and validation frameworks. Partner with architects and design teams to provide performance-driven recommendations, lead technical initiatives, improve methodologies, and mentor engineers.

Develop and optimize Windows kernel-mode GPU drivers for Intel integrated and discrete graphics. Implement device activation, power management, workload scheduling, firmware, and SOC-related features. Debug complex issues across driver and hardware layers, analyze telemetry, and create automated testing and validation frameworks. The role requires C/C++, Python, GPU architecture, Windows driver, and low-level systems expertise.

Leads the AI strategy and architecture for supply-chain planning operations. Builds production machine-learning, data-stitching, anomaly-detection, scenario-modeling, alerting, and agentic workflow capabilities across enterprise planning systems. Establishes MLOps, governance, observability, security, and explainability practices while partnering with planners, engineering, manufacturing, procurement, finance, and executives to deliver trusted operational decision support.

Build and operate secure, reliable platform infrastructure for scientific data pipelines, AI agents, research workflows, and customer products. Responsibilities include cloud infrastructure, workflow orchestration, secure agent execution, reproducible data pipelines, CI/CD, identity and access management, observability, database recovery, and production troubleshooting. The role partners with scientists, ML researchers, and product engineers to automate operational processes and improve developer productivity.

Build production-grade data pipelines and shared infrastructure for laboratory, imaging, omics, mass spectrometry, ADME, chemical, and clinical data. Unify scientific datasets, implement statistical quality controls, maintain reproducible schemas and versions, and provide dashboards, APIs, monitoring, and alerts. Partner with scientists, ML researchers, and engineers to investigate failures, improve workflows, and deliver validated datasets for drug discovery models and AI agents.

Build reliable infrastructure for AI agents conducting long-horizon scientific investigations in drug discovery. Responsibilities include agent runtimes, orchestration, sandboxed execution, retrieval and memory, observability, evaluation, operational safeguards, and customer-facing deployment. The role partners with scientists and ML researchers to create tools, workflows, rollouts, reward instrumentation, and training pipelines. Strong Python and backend engineering experience is expected, along with familiarity with cloud infrastructure, data systems, LLM applications, agent evaluation, and research infrastructure.

Optimize multimodal foundation-model inference across AMD GPUs, TPUs, embedded Jetson hardware, consumer GPUs, and specialized accelerators. Responsibilities include kernel tuning, compilation, quantization, pruning, distillation, model parallelism, benchmarking, regression detection, numerical validation, and hardware cost-performance analysis. The role also supports real-time on-robot inference, hardware selection, and collaboration with research, robotics, and infrastructure teams.