Machine Learning Jobs in North Las Vegas, NV

Lead end-to-end delivery of AI and ML services within Managed Services. Manage project planning, budgeting, execution, and quality. Mentor and develop team members, cultivate client relationships, implement model monitoring and operationalization practices, and collaborate with architects and engineers to ensure compliant, reusable AI solutions.

Lead design and execution of AI-enabled workforce management forecasting, capacity planning, and data pipelines for CX. Build governance, dashboards, and predictive models, partner cross-functionally, monitor real-time alerts (PagerDuty), and provide staffing and hiring recommendations to optimize contact center operations.

Lead AI engineering teams to design, build, and deploy end-to-end AI solutions. Manage client relationships, plan and deliver projects, mentor staff, drive adoption of cloud-native microservices, CI/CD, vector databases and orchestration tools, and ensure high standards of quality, innovation, and compliance.

Lead teams applying advanced data science and machine learning to large-scale claims, clinical, and member data to generate actionable insights that reduce cost of care and improve population health. Manage client relationships, mentor staff, ensure project delivery and quality, develop payer-focused analytics products, present findings to senior stakeholders, and promote continuous improvement and knowledge sharing.

Own strategy and roadmap for Crunchbase's customer-facing AI data layer. Identify and validate model-derived data products, lead cross-functional development, set quality standards, drive adoption and monetization, and monitor performance and product economics.

Senior-level engineer delivering analytics and ML-Ops solutions for healthcare payers. Design and implement analytics platforms on Databricks, develop CI/CD and provisioning patterns, collaborate with data scientists, mentor juniors, and build client relationships to drive actionable insights and operational improvements.

The Sales Enablement Lead will design the sales strategy, create enablement assets, and align sales priorities with product offerings, ensuring effective execution across the revenue lifecycle.

Lead development of motion planning and prediction ML models for Level 4 autonomous vehicles. Design and productionize behavior models using deep learning, generative AI, and reinforcement learning. Deploy solutions to vehicle fleets, collaborate with perception, simulation, data platform and integration teams, and report progress to leadership.

Lead development of rigorous evaluation frameworks and metrics for autonomous systems, analyze large on-road and simulation datasets, detect metric drift, prototype statistical and ML methods, correlate simulation and real-world results, and drive actionable improvements to testing, sampling, and launch-readiness decisions while mentoring teammates and advising stakeholders.

Lead strategy and roadmap for Payments and Fraud platforms, manage product managers, drive improvements in payment success, fraud mitigation, routing, reconciliation, and real-time monitoring. Align stakeholders across Risk, Compliance, Finance, and Data Science while delivering measurable growth, reliability, and cost reductions.

The Director of Computer Vision will lead technical vision, research, and execution in developing scalable AI-driven computer vision solutions, managing teams, and enhancing performance metrics.

Apply statistical and analytical methods to identify revenue opportunities, build predictive and explanatory models, design and evaluate experiments, analyze and integrate data sources, and translate findings into actionable recommendations for cross-functional stakeholders across hotel, F&B, pool, spa, and gaming operations.

Lead Lean transformation initiatives and AI coordination to enhance process excellence across multiple business functions, coaching teams and ensuring integration with operational standards.

Build and optimize high-performance systems for large-scale ML training: profile and remove bottlenecks, optimize distributed training, develop GPU kernels (Triton/CUDA), and engineer high-throughput data pipelines to reduce time-to-convergence and improve reliability and cost-efficiency.

Manage distributed machine learning workloads using Slurm and Kubernetes, ensuring cluster operations and mentoring engineers in best practices.

Build and scale ML development infrastructure and Agentic AI tooling. Optimize distributed training (PyTorch/DDP), improve GPU utilization, design multi-cloud Kubernetes environments, and collaborate with ML researchers and engineers to streamline the ML lifecycle.