Top Machine Learning Jobs in Reno, NV

Lead the design and deployment of RL post-training systems, aligning model behavior with user value and business objectives, while mentoring applied scientists and engineers.

As a Principal Machine Learning Engineer, you will develop multimodal AI experiences, design agent interactions, and lead cross-team initiatives while ensuring AI safety and reliability.

The VP will transform R&D operations by embedding AI into product workflows, enhancing efficiency and decision-making across teams. They will lead system redesign, implement AI-driven analytics, and establish KPIs for continuous improvement.

As a GTM Engineering Intern, you will automate workflows, design demos, and develop AI-powered tools for market outreach while collaborating with sales and engineering teams.

The Senior Data Scientist will develop scalable ML models, analyze large datasets for insights, and collaborate with teams to enhance business performance through data-driven solutions.

The Data Scientist will develop and improve models and LLM systems for security applications, focusing on production readiness and experimentation. Responsibilities include building classifiers and evaluating models.

As a Staff Software Engineer, you'll lead the development of AI-driven automations, improving data integration and platform capabilities for cybersecurity teams with over 8 years of experience in complex backend applications and distributed systems.

The Customer Success Manager drives the adoption of AI solutions in life sciences by managing implementations, developing customer satisfaction strategies, and serving as a trusted advisor.

Lead operationalization of AI strategy by implementing scalable AI platforms, standards, and governance. Identify and prioritize high-impact AI and analytics use cases, drive end-to-end delivery into production, embed AI into business processes, manage AI teams, mentor practitioners, and evaluate emerging AI/GenAI technologies to ensure responsible, secure, and measurable business impact.

Lead implementation of enterprise AI strategy and platforms, prioritize and deliver high‑impact AI and analytics use cases end-to-end, manage AI teams, embed responsible AI governance, and drive adoption and capability building across business and technology stakeholders.

Lead vision, strategy, and roadmap for pricing platforms, owning internal Rating Plan Manager and tooling to speed model deployment, improve workflow reliability and observability, enable self-service experimentation, and support regulatory/state-level pricing needs through cross-functional collaboration.

Lead statistical activities for clinical trials from protocol design through reporting. Provide statistical oversight, collaborate with cross-functional teams and CROs, apply SAS/R (and ML) for analyses, automate processes, and mentor junior statisticians.

Design, develop, and deploy causal inference and predictive models (uplift, synthetic control, DML) for customer loyalty, churn, and LTV. Build foundational data models and scalable features, perform large-scale SQL ETL, operationalize models in cloud, partner with stakeholders, and present insights to drive revenue and loyalty program optimization.

Lead engineering strategy and delivery for customer-facing web and mobile platforms; drive AI-native transformation, increase conversion and speed-to-fund, raise engineering talent and velocity, ensure secure and compliant architectures, partner with product and cross-functional teams to deliver measurable business outcomes.

Design, develop, and implement AI/ML models; analyze large datasets; integrate AI solutions into existing systems; apply MavLink for autonomous systems; collaborate across teams and improve ML development processes.

Support analytics by cleaning, validating, and transforming data; build dashboards and KPI reports; perform trend, cohort, and A/B testing analyses; improve data pipelines and forecasting; present insights to cross-functional teams.

Lead architecture and development of a SaaS platform for data center energy procurement. Build forecasting, contracting, and monitoring modules; implement AI tools on AWS (Bedrock, SageMaker); hire and mentor engineers; translate business logic into scalable systems; deliver demos to investors and customers.

Review, label, and QA datasets for AI/ML training; maintain trackers, databases, and dashboards; follow annotation guidelines and SOPs; prepare and clean data; document processes; collaborate to resolve data queries while ensuring confidentiality and timely delivery.

Lead end-to-end product for an enterprise AI security product: set roadmap, build product processes, run customer discovery with security practitioners, partner with engineering to ship ML/AI features, and enable GTM. Build the product function from scratch and define metrics, specs, and AI quality measures to drive adoption, retention, and expansion.

The Lead Bioinformatics AI Scientist will lead AI and GenAI R&D initiatives to develop advanced clinical testing and genomic data analysis platforms, leveraging expertise in bioinformatics and machine learning.

The SVP of AI Innovation - Web Scraping will lead the development of AI-driven web scraping strategies, focusing on data acquisition, technology enablement, compliance, team building, and cross-functional collaboration for new data services.

The Product Manager will identify process improvements and innovations, utilize automation and AI, and manage the product lifecycle to enhance productivity and deliver business value.

The Patent Agent drafts and prosecutes U.S. and international patent applications, working primarily in software and AI technologies with a focus on high-volume technical areas.

Draft and prosecute U.S. and international patent applications, focusing on electrical, mechanical technologies, and software. Work remotely with flexible arrangements.

The AI Application Engineer will design and deploy AI systems, collaborate with teams, implement Generative AI solutions, and promote continuous learning in AI practices.