Top Machine Learning Jobs in Las Vegas, NV

Build and maintain backend and full-stack marketplace features while supporting ML Ops infrastructure (model deployment, feature stores, real-time pipelines, monitoring). Collaborate with data scientists to bring models to production, write well-tested code, address performance and scalability issues, and contribute to technical planning and cross-functional communication.

Build and maintain a capacity management portal and observability features for ML training and inference. Partner with researchers and PMs to turn requirements into intuitive UIs, integrate backend APIs across schedulers and training frameworks, develop internal tooling, and drive architectural improvements and best practices to improve efficiency and model quality visibility.

The Senior Machine Learning Engineer will build and optimize ML systems for CRM marketing, collaborating with cross-functional teams to impact customer engagement and product functionality.

Own the advertising recommendation engine, including ranking quality, optimization objectives, guardrails, metrics, and model roadmap. Lead experiment design, inference standards, holdouts, rollback criteria, and production launches. Evaluate model performance and business impact with data science and stakeholders. Partner across ML, analytics, ad operations, pricing, identity, telemetry, and commercial teams. Apply advertising economics, ranking systems, SQL, and data-platform expertise to improve monetization for mobile apps and games.

The Senior AI/ML Engineer will develop AI systems for enterprise decision-making, focusing on LLMs and real-time data processing. Responsibilities include building detection systems, insight engines, evaluation frameworks, and collaborating with engineers to ensure robust AI product engineering.

Build and scale the ML infrastructure platform for humanoid robotics, including data processing, training, evaluation, deployment, model management, observability, and reproducible workflows. Develop cloud-native Kubernetes workflows, model registries, feature stores, experiment tracking, developer APIs, CLI tools, and ML CI/CD systems. Collaborate with AI researchers, robotics engineers, and data platform teams while mentoring junior engineers and shaping platform architecture, reliability, and scalability.

Design and build a hybrid rule-based and machine-learning system that maps contractor free text to a four-tier SAP catalog hierarchy, detects anomalies, learns from operator corrections, and exposes the model through a microservice. Responsibilities include architecture documentation, accuracy-threshold recommendations, classification pipeline development, model evaluation, feedback-loop design, and collaboration during Agile sprint reviews.

Lead AI/ML strategy, architecture, development, deployment, and operationalization for CMS mission environments. Design predictive analytics, automation, and decision-support systems; oversee data pipelines, MLOps, model validation, explainability, and bias mitigation. Integrate secure AI solutions with cybersecurity, governance, compliance, and risk management requirements. Develop RAG and agentic AI capabilities, transition prototypes to production, advise stakeholders, support audits and governance reviews, and mentor engineering teams.

Designs, builds, deploys, and operates production AI/ML and LLM systems. Responsibilities include developing RAG pipelines, fine-tuning and evaluating models, managing end-to-end MLOps lifecycles, establishing monitoring and governance practices, mentoring engineers, and leading complex cross-functional client and internal initiatives.

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.

The Senior ML Engineer will develop and deploy full-stack AI solutions, manage LLM integration, and collaborate closely with defense clients to enhance real-world applications in secure environments.

Lead design, development, and production deployment of ML and algorithmic solutions. Own end-to-end ML systems, support distributed production reliability, communicate tradeoffs, and apply generative AI, LLM fine-tuning, RAG, and agentic techniques to product problems.

Build and productionize AI systems for logistics: multi-agent copilots, forecasting/optimization models, evaluation and observability, continuous learning loops, and operational reliability. Mentor engineers and make build-vs-rule tradeoffs while shipping high-impact ML services.

Define and execute predictive modeling and ML strategy for credit risk, servicing, and collections. Lead cross-functional initiatives, oversee model development, deployment, and monitoring, advise senior leadership on data strategy, represent analytics in regulatory and audit forums, and mentor peers while fostering organizational partnerships.

Lead and execute ML/AI strategy, design and deploy production-grade ML and GenAI solutions, mentor MLEs, collaborate with cross-functional teams, and build scalable, secure ML/LLM platforms and inference pipelines to drive business impact.

As a Senior MLOps Engineer, you will manage production ML systems, define lifecycle strategies, optimize ML pipelines, and collaborate with cross-functional teams to enhance ML operations.

Develop and productionize computer vision and deep learning models across FloVision’s products. Responsibilities include building ETL pipelines, preparing and annotating datasets, feature engineering, statistical analysis, model training and evaluation, deployment, monitoring, and improving data quality. The engineer will collaborate across machine learning, software, hardware, product, and annotation teams, work autonomously in a startup environment, and occasionally travel to production sites and facilities.

Build and deploy deep-learning models for financial market data, owning the full workflow from raw data and experiments through daily production inference. Responsibilities include managing small-scale GPU infrastructure, conducting leakage-proof and regime-aware evaluations, reproducing research, reading recent AI literature, and communicating findings. The role requires strong ML fundamentals, Python and PyTorch expertise, hands-on infrastructure skills, rigorous experimentation, and self-directed remote work. Trading experience is not required.

Analyze, clean, and validate complex healthcare data; develop statistical, machine learning, and AI models; implement agentic workflows, RAG solutions, and model fine-tuning; deploy and monitor models in production; collaborate with technical and business teams; conduct research; and document and communicate findings.

Leads the technical direction of an autonomous vehicle perception stack. Responsibilities include architecting scalable perception systems, developing scene detection and runtime activation, improving robustness against sensor degradation and adverse weather, integrating multimodal models, managing ML and data pipelines, monitoring field performance, leading testing and feature delivery, and mentoring engineers. Requires strong robotics, computer vision, machine learning, real-time systems, software architecture, and team leadership experience.

Operate and improve enterprise AI/ML platforms across Palantir Foundry, AWS Bedrock, and Amazon SageMaker. Responsibilities include deployment, monitoring, observability, automation, governance, model lifecycle management, incident response, root cause analysis, CI/CD, infrastructure as code, and production reliability. The role provides technical leadership and mentorship while partnering with data, infrastructure, security, architecture, product, and engineering teams to support cloud-based AI workloads and Generative AI adoption.

Audit intermediate linear algebra exam questions for technical accuracy, remove ambiguous distractors, verify coding prompts and rubrics, create NumPy edge cases, and return a corrected JSON exam. The work requires expertise in machine learning, PCA, SVD, eigenvalues, matrix operations, Python, NumPy, and structured JSON validation.

Lead the strategy and implementation of AI solutions in R&D, driving adoption and improving operational efficiency through advanced analytics. Collaborate with cross-functional teams to design AI/ML models and evaluate new technologies.

Design, prototype, and ship LLM- and agent-powered AI features for observability. Rapidly iterate with real users, integrate AI workflows with tooling and alerting, collaborate cross-functionally, and ensure scalable, production-ready solutions in cloud-native environments.

Lead ML technical strategy for Live Ads, designing and deploying forecasting, targeting, bidding, auction, and yield-optimization models. Build and evaluate large-scale production ML systems, create online/offline evaluation frameworks, partner with product to set objectives and trade-offs, and communicate results to stakeholders and leadership.