Top Senior Machine Learning Jobs in Las Vegas, NV

Design, build, and operate production-grade AI systems using ML, LLMs, RAG, and agentic workflows. Define evaluation strategies, implement data/retrieval/orchestration layers, apply MLOps/LLMOps, and partner with stakeholders to prioritize, monitor, troubleshoot, and improve AI solutions balancing quality, latency, cost, reliability, and maintainability.

The Engineering Manager will lead teams in developing and managing Blackbaud's Data & AI platform, focusing on scalability, security, and cost-efficiency.

The role involves developing and integrating AI features into Cribl's products, collaborating with teams, and maintaining operational efficiency.

Lead development of digital twin and simulation models for warehouse operations, deploy AI-driven decision systems, build forecasting and optimization models, validate experiments linking operational metrics to financial impact, and partner with engineers, operations, and external AI/academic partners to scale innovations across fulfillment networks.

Lead Data Scientist responsible for creating ML-driven risk assessments and AI workflows within a SaaS security platform, ensuring effective decision-making and system reliability.

Lead end-to-end delivery of LOIS by owning the master program plan, coordinating cross-functional teams (engineering, product, design, ML, data, SRE, operations), resolving blockers and dependencies, establishing lightweight delivery infrastructure and tooling, partnering on release planning, and maintaining transparent status, risks, and accountability to deliver complex features to customers.

Lead fraud detection and mitigation for a conversational BNPL product. Define fraud typologies, own repayment and chargeback analytics, manage fraud KPIs, design operational policies, collaborate on ML models and decisioning workflows, and coordinate SARs and compliance reporting.

Lead credit analytics for the Send Now Pay Later product: build and maintain credit policies, pricing models, vintage and NPV analyses, executive reporting, model integration and testing, and explore alternative data to optimize approval rates and portfolio health.

Lead fraud detection and mitigation for a conversational Send Now Pay Later product: define first-party fraud typologies, own repayment and chargeback analytics, manage fraud KPIs, design operational policies, integrate ML and decisioning tools (Taktile), and coordinate SARs and compliance reporting.

Lead credit analytics for the Send Now Pay Later product: build and optimize valuation, vintage and NPV models; manage credit policies, portfolio monitoring, and NPL reporting; integrate and test scoring models with engineering/data science; and research alternative data to improve predictive performance.

Lead fraud detection and mitigation for a conversational BNPL product: define fraud typologies, analyze repayment/chargeback trends, own fraud KPIs, design policies, collaborate on ML models and decisioning workflows, and coordinate SARs with Compliance.

Lead fraud risk for a conversational lending product (SNPL): define first-party fraud typologies, analyze repayment and chargeback trends, own fraud KPIs, design automated/manual blocking policies, coordinate ML/rules with central Fraud and Data teams, and liaise with Compliance on SARs and loan abuse.

Lead quantitative credit analytics for a Send Now Pay Later (SNPL) product: build and maintain credit policies, pricing and NPV models, vintage analysis, portfolio monitoring, model integration/testing, and alternative data evaluation to optimize approval rates and manage portfolio health.

The Engineering Manager for Smart Signals leads a team, manages execution, and maintains technical depth in backend systems for fraud detection. They shape strategy and coordinate across teams to enhance product quality.

The Senior Backend Data Engineer will extend data ingestion and processing pipelines, build APIs and backend services, and provide technical leadership.

Design, optimize, and scale ClickUp's search infrastructure using OpenSearch/ElasticSearch. Improve relevance, speed, and real-time indexing; build vector/semantic search capabilities; create measurement frameworks; integrate search with AI and product features; troubleshoot at scale; and mentor junior engineers to maintain high search quality under heavy load.

Design, scale, and optimize ClickUp's search infrastructure using OpenSearch/Elasticsearch. Improve relevance, speed, and real-time indexing, build vector search capabilities, create evaluation frameworks, and collaborate with AI/backend/product teams to integrate and troubleshoot search at scale.

The Senior AI Data Engineer will design and build scalable data pipelines, ensure data quality, and support AI product development through a unified analytical data framework.

The Principal Data Scientist will design, develop, and deploy machine learning models, innovating methodologies and collaborating across teams to enhance financial decision-making and risk management.

Lead technical pre-sales and enterprise rollouts by owning infrastructure strategy, PoCs, migrations (on‑prem to cloud), DMS/CRM integrations, InfoSec discussions, and stakeholder engagement with C-level sponsors to enable scalable deployments and upsell opportunities.

As a Senior Data Scientist, you will identify business opportunities, build machine learning solutions, partner cross-functionally, and translate analyses into actionable insights.

The Solutions Architect will bridge customers and sales, ensuring the technical fit of software solutions, leading demos, and managing deployments effectively.

The Senior Manager will oversee the fraud lifecycle, optimize detection strategies, conduct fraud reviews, collaborate with teams, and manage risk reporting.

Design and build backend systems and APIs for AI-driven products, ensuring scalability, reliability, and integration of ML models while collaborating with engineering and product teams.

Lead design and scaling of a unified fraud and risk platform. Own roadmap, fraud metrics, SQL-based analysis, A/B testing, real-time detection models, and cross-functional delivery to balance customer experience and risk mitigation.