Top Machine Learning Jobs in San Diego, CA

Lead product initiatives from discovery through launch for healthcare SaaS products using analytics and ML. Own roadmap, prioritize backlog, partner with engineering/design/data, use qualitative and quantitative evidence, and drive adoption and measurable customer impact.

Build and operate ML training and serving infrastructure by designing, developing, and launching backend systems. Collaborate across teams to decompose projects, support operations and on-call, create monitoring and metrics, perform code reviews, and contribute to developer velocity and platform reliability.

Own Cash Application and Deduction Resolution Management products end-to-end. Lead discovery, define requirements, prioritize roadmap, partner with engineering/design/sales, ensure financial accuracy, build ERP integrations, drive launches, and maintain product quality and customer-focused outcomes.

Lead implementation of enterprise AI platforms, governance, and high-impact analytics use cases. Drive end-to-end delivery from experimentation to production, mentor teams, embed responsible AI practices, and partner with cross-functional stakeholders to operationalize AI across the business.

The Enterprise Architect leads architectural strategy and integration for technology and processes while aligning IT investments with business goals. Responsibilities include overseeing cloud modernization, governance, and communication with stakeholders.

The Director of AI & Automation leads GT's enterprise-wide AI and automation strategy, driving initiatives to enhance operational efficiency and improve service delivery in a regulated environment.

The Director of Product Management will oversee the product lifecycle for AI products, focusing on customer collaboration, rapid prototyping, and product strategy. The role involves leading product teams, driving execution, and shaping the product culture.

The VP of Marketing will lead the marketing ecosystem to drive growth, define competitive advantages, influence product design, and mentor a diverse team towards a mission-driven culture.

The SAP Innovation Architect will design SAP automations, mentor engineers, and drive innovation in AI-driven SAP operations, while ensuring scalability and security in integrations.

Lead the development of agentic AI systems for real estate, focusing on long-horizon, supervised experiences. Architect collaborative systems and define success metrics while mentoring a small team.

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.

Lead design, development, and deployment of end-to-end recommendation systems. Build, tune, and evaluate scalable recommendation algorithms, integrate ML into products, lead projects, mentor teams, and communicate solutions to clients and stakeholders while driving innovation in replenishment and supply chain analytics.