Top Machine Learning Jobs in San Diego, CA

Lead end-to-end supply chain transformation engagements, advising executives and directing multidisciplinary teams to improve planning, procurement, logistics, inventory, and supplier performance. Design future-state architectures, build business cases, drive implementation, grow client relationships, and scale practice capabilities while delivering measurable operational and financial impact.

Lead data strategy and analytics for cognitive assessment studies: design algorithms and models, implement CDISC-compliant data transfers (SDTM/ADaM), produce statistical analysis plans and scientific reports, build and maintain analytics pipelines, dashboards, and visualizations, ensure data quality and QC, and present results to sponsors while managing multiple projects independently.

Lead perception vision, strategy, and roadmap for AV perception. Drive advances in detection, classification, tracking, fusion, and prediction; design sensor suites; define KPIs; hire and manage engineering teams; coordinate cross-functional deployment and issue resolution to ensure robust commercial autonomy performance.

Lead product and design teams to define strategy, roadmaps, and deliver AI-enabled ESG data products for commercial real estate. Collaborate with engineering, sales, and marketing, set AI accuracy and reliability benchmarks, manage budgets and resources, and represent product to enterprise customers while ensuring regulatory-grade data quality and explainability.

Design, build, and maintain scalable AI/ML pipelines for data processing, model training, and deployment. Collaborate with data scientists and engineers to implement and optimize AI solutions, troubleshoot pipeline issues, document best practices, and develop MVP code to transition into production-ready systems.

Lead global sales enablement strategy and programs to drive revenue, product adoption, and GTM readiness. Oversee product-aligned enablement teams, launch readiness, content and demo strategy, CRM and tool adoption, measurement frameworks linking enablement to outcomes, and integration of AI into enablement.

Lead Billtrust's Applied Intelligence product strategy, build and scale AI product teams, define governance and evaluation standards, translate frontier AI into production-grade AR solutions, and represent the company externally to drive commercial impact and customer co-creation.

The Associate Director, AI/ML Engineering will lead the development and deployment of AI solutions, manage multi-agent systems, and collaborate across functions to optimize AI/ML processes within a biopharmaceutical context.

The Data Scientist II will design, build, and deploy machine learning models and data products, translating business problems into data-driven solutions, while collaborating with various stakeholders and ensuring reliable, production-ready outcomes.

The Enrollment Advisor helps prospective patients through the onboarding process, providing information and support throughout their enrollment in Revero's program.

Provide technical advisory and solution feasibility analysis for enterprise AI initiatives. Evaluate AI engineering approaches, integration complexity, reusable components, and architectural tradeoffs. Support prototyping guidance, vendor capability evaluation, and surface governance, risk, and technical constraints to inform enterprise decisions.

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.

Remote technical recruiter responsible for sourcing and hiring software engineers and niche technical talent for startups. Tasks include sourcing via LinkedIn and job boards, shortlisting, verifying technical fit, researching companies and market trends, developing and executing recruitment strategies, and communicating with candidates and stakeholders. Immediate joiners preferred.

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.

Lead architecture, design, development, deployment, and optimization of an agentic AI platform. Partner with product, data, security, and business teams to productionize AI solutions, establish engineering best practices, mentor teams, and drive reusable AI services, automation, and operational improvements to deliver business impact.

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 machine learning initiatives, oversee product development, improve execution pace, enhance team reliability, and foster team growth at Hightouch.

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.

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.

Define and execute the companys AI strategy; build and lead the data science, ML engineering, and AI teams; govern intake, prioritization, and AI guardrails; advance production ML, MLOps, LLMs and agentic workflows; partner cross-functionally to deliver measurable ROI; and report AI impact and roadmap to the CEO and board.

Develop, implement, and deploy scalable machine learning models for healthcare solutions. Collaborate cross-functionally to integrate models, analyze data to improve performance, ensure data quality, and stay current with ML advancements to support patient care and operational efficiency.

Design, implement, and own a reusable on-device ML inference platform for resource-constrained devices. Integrate inference into RTOS firmware, develop drivers and DMA paths, debug on real hardware, train and optimize models for microcontroller-class hardware, and ship features to production while raising team expertise and using AI-assisted tooling.

Build, train, fine-tune, evaluate, and deploy machine learning and LLM models for production. Create monitoring, evaluation, and feedback tooling, evaluate new techniques, and collaborate cross-functionally to ship ML-powered features.

Develop and productionize consumer-facing AI features using LLMs. Collaborate across the AI stack, run A/B tests and evaluations, improve model reliability and guardrails, and optimize latency, cost, and scalability of production AI systems.