Top Senior Machine Learning Jobs in Las Vegas, NV

Lead strategy and roadmap for DTNs upstream intelligence portfolio, especially Farm Intelligence. Conduct customer discovery, define product metrics, prioritize features, and partner with Engineering, Design, Data Science, and Commercial teams to deliver data-intensive, AI-enabled B2B SaaS products. Drive launches, adoption, and continuous optimization through experimentation, analytics, and stakeholder alignment.

Lead the design, development, and deployment of scalable machine learning and AI systems for enterprise clients, guiding architecture and mentoring engineers.

Lead the architecture, implementation, integration, and optimization of Oracle EPM Planning solutions. Design planning, budgeting, forecasting, modeling, security, metadata, and reporting frameworks; configure Oracle EPM modules; and apply AI-driven forecasting capabilities. Lead requirements workshops, testing, deployment, stakeholder presentations, and pre-sales activities. Mentor consultants and developers while advising finance and executive stakeholders on enterprise planning transformation initiatives.

Design, build, and deploy production-grade ML systems for ICS/xOT cybersecurity, including threat detection, anomaly detection, NLP/LLM applications, and scalable data pipelines with observability and MLOps practices for cloud and on-prem environments.

Design, build, and deploy production ML systems for ICS/xOT cybersecurity, including threat detection, anomaly detection, NLP/LLM features, data pipelines, observability, MLOps, and containerized deployments across cloud and on-prem environments.

Develop and optimize machine learning and statistical models for fraud detection and anti-money laundering. Analyze large datasets to identify fraudulent patterns, build scalable data pipelines and features, evaluate model performance, and collaborate with engineering, product, and operations teams. Communicate insights to technical and non-technical stakeholders while maintaining data privacy and security compliance. The role offers significant ownership in building Oscilar’s early machine learning stack within a remote-first startup.

The Senior Machine Learning Researcher will design experiments, research new algorithms, and collaborate on deploying models for computer vision and robotics applications.

Leads enterprise sales strategy and revenue growth for AI-powered eDiscovery, legal technology, compliance, analytics, and data intelligence solutions. Builds pipelines, manages complex consultative sales cycles, develops executive client relationships, expands strategic accounts, and positions responsible AI adoption. Partners with product, operations, legal, security, marketing, and executive teams on solution development, pricing, contracts, implementation, and market expansion across corporations, law firms, government, and regulated industries.

As an AI Applications Engineer, you will bridge AI research and implementation, driving experiments, building infrastructure, and releasing products to improve user experience and metrics.

Leads technical solution development for military and federal health capture and proposal efforts. Designs secure cloud, DevSecOps, EHR modernization, interoperability, data, and analytics architectures; develops technical volumes, graphics, compliance matrices, staffing estimates, demonstrations, and presentations. Ensures security and compliance alignment, supports pricing strategy, collaborates across capture and delivery teams, and mentors architects and subject-matter experts.

Oversee development, validation, monitoring, and governance of consumer credit risk models used for loss forecasting, credit decisioning, regulatory reporting, and risk management. Apply statistical, econometric, and machine learning methods to PD, LGD, and EAD models; support CECL, stress testing, and macroeconomic scenario analysis. Assess model performance, data quality, and compliance while communicating findings to senior leaders, auditors, regulators, and other stakeholders.

Leads strategy, roadmap, development, and adoption of analytics and AI products for Ford Integrated Services. Owns reporting platforms, dashboards, governance, metrics, support models, and generative AI initiatives. Partners with senior stakeholders and cross-functional teams including engineering, design, data science, and analytics to deliver actionable insights, improve subscriptions performance, and establish trusted business intelligence solutions.

Lead and grow a data science team focused on customer intelligence, personalization, product analytics, marketing optimization, and customer lifetime value. Develop predictive and segmentation models, analyze web and mobile engagement, design experimentation programs, and apply causal inference, survival analysis, forecasting, Bayesian methods, NLP, and machine learning. Partner with Product, Marketing, Editorial, Data Operations, and Engineering to translate customer behavior into scalable products, actionable recommendations, and measurable business impact.

Leads a backend engineering team responsible for scalable, reliable systems. Balances people management, technical direction, architecture, and execution; hires, coaches, and develops engineers; guides complex initiatives from design through production; and partners with Product and Engineering stakeholders to improve system reliability, performance, maintainability, and team productivity.

Lead Elastic’s strategy, vision, and multi-quarter roadmap for vector databases, embedding and reranking models, vector indexing, semantic ingestion, and search capabilities. Conduct market and competitive research, define product metrics, prioritize features, and partner closely with engineering, cloud, and applied research teams. Guide technical trade-offs involving recall, latency, index size, ingestion throughput, model lifecycle, and total cost of ownership across serverless and self-managed deployments.

Own the technical direction, roadmap support, and prioritized backlog for Geo Addressing SDK and related addressing products. Serve as the technical bridge among Product, Engineering, QA, Support, customers, and partners. Investigate escalations, validate fixes, define acceptance criteria, improve address matching quality, evaluate data sources, support release communications, and lead demos. The role requires expertise in geocoding, address validation, APIs, software architecture, enterprise data quality, and AI-assisted product workflows.

Owns a portfolio of Data-as-a-Service products end to end, including vision, roadmap, requirements, agile delivery, pricing, packaging, distribution, privacy compliance, and go-to-market execution. Partners with engineering, data science, legal, marketing, sales, and customer success to deliver data enrichment, identity, and audience products through APIs, clean rooms, marketplaces, integrations, and self-service channels. Uses launch metrics and customer insights to guide investment decisions.

Own the end-to-end rebuild of Nextdoor’s consumer search experience, including query understanding, retrieval, ranking, result quality, organic and sponsored content blending, and monetization. Define search relevance metrics, partner with engineering, data science, ML, research, design, and marketing teams, and use A/B testing to improve engagement and revenue while protecting user experience.

Build and lead scalable frontend products for Airbnb’s Community Support Engineering organization. Responsibilities include architecting web applications, developing reusable frontend components, designing supporting server-side APIs, implementing testing and experimentation, optimizing accessibility and performance, collaborating with product, design, machine learning, and generative AI teams, shaping technical direction, and mentoring junior engineers.

Leads commercial, privacy, and product legal functions for a cloud technology company. Drafts and negotiates complex SaaS, partnership, reseller, and technology agreements; advises on global privacy, data protection, AI, intellectual property, and product development issues; develops contracting processes and playbooks; partners with cross-functional teams; and manages, mentors, and develops legal professionals.

Develops, calibrates, validates, and maintains credit risk models, including PD, LGD, and EAD models, for lending, collections, loss forecasting, capital planning, and stress testing. Analyzes large datasets, evaluates model performance, documents model changes, supports systems testing, and communicates statistical insights to stakeholders and management. Collaborates across the credit union to improve risk-return strategies and reporting.

Close $1M in net-new bookings by selling AI transformation to CEO-led buying committees. Generate outbound pipeline, co-sell initially with the founder, then independently close complex mid-market deals. Build a repeatable sales playbook covering ICP, discovery, multi-threading, pricing, objections, and close plans. Feed deal insights into company packaging and pricing, with a path to build and lead the future sales team.

Lead design and implementation of high-throughput, low-latency programmatic bidding systems. Collaborate with ML engineers to deploy real-time inference, optimize distributed microservices, run experiments and quantitative analysis, and partner with product to ship scalable features. Foster operational excellence, mentor engineers, and continuously improve performance and reliability.

Own the product strategy, roadmap, team, customer relationships, and P&L for Teamworks’ Coaching, Player Personnel, Recruiting, and Intelligence portfolio. Lead AI strategy across sports markets, integrate acquired products, align engineering, design, data science, marketing, and sales, and convert proprietary sports data into AI-powered workflows. Represent the business with teams, leagues, and partners while driving multi-market growth and product execution.

Leads technical sales and executive evangelism for Verint’s Open CCaaS and CX Automation platform. Acts as a trusted advisor to C-suite customers, articulating ROI, AI, data science, and platform capabilities. Partners with Product, Sales, Solutions, Services, Marketing, and Customer Success teams to drive adoption, expansion, thought leadership, and opportunity closure. Requires deep contact center operations experience, strong data architecture knowledge, executive communication skills, and extensive travel.