Top Machine Learning Jobs in San Diego, CA

Own end-to-end analytical lifecycle to develop and optimize business, risk, and growth strategies. Monitor portfolio and macro trends, build and interpret ML-driven models, lead cross-functional launches, mentor junior analysts, and build AI-assisted workflows to improve decisioning and measurement across products and geographies.

Develop optimized GPU algorithms and high-performance machine learning solutions for vector search, databases, and data processing. Responsibilities include designing, implementing, benchmarking, debugging, and optimizing C++, CUDA, and Python libraries; developing clustering and visualization algorithms; collaborating cross-functionally; and contributing to open-source projects such as cuML, cuVS, and RAFT.

Develop agentic AI systems and machine learning methods for drug discovery, including molecular and tabular in-context learning, few-shot adaptation, foundation models, and biological data analysis. Lead research projects from task definition and dataset construction through model training, benchmarking, ablations, and scientific validation. Collaborate with chemists and biologists, build reproducible research software, scale training pipelines, and translate successful methods into drug discovery capabilities and scientific publications.

Supports federal, state, local, tribal, and territorial emergency management planning through geospatial and data analytics. Develops emergency operations, hazard mitigation, and continuity plans; applies FEMA planning processes; facilitates stakeholder coordination; supports exercises, disaster activations, and deployments. Performs quantitative and qualitative analysis, develops database and modeling solutions, applies AI and machine learning techniques, and ensures compliance with government regulations. This is an on-call, non-full-time role requiring frequent travel and extended deployments.

Lead design, build, and deploy scalable, reliable Java-based microservices in a cloud-native, event-driven AWS environment. Own backend feature design, code quality, automated testing, and FedRAMP release processes. Mentor engineers, participate in on-call/TechOps, integrate AI tools into development, and drive architecture, observability, and cross-team initiatives.

As a Customer Success Engineer, you'll drive adoption and satisfaction for enterprise customers using Deepgram's voice AI technology, fostering strong relationships and aligning technical solutions with business objectives.

Own product areas for an autonomous banking platform, translating backend capabilities (APIs, async workflows, ML) into user-facing features. Conduct customer discovery, define technical requirements and acceptance criteria, prioritize backlog, partner with engineering on scoping and de-risking, and validate outcomes using feedback loops and analytics.

Lead the Product and Design organization, define product vision and roadmaps, prioritize strategic investments, and align cross-functional teams to deliver safe, scalable robotic delivery experiences. Build and develop high-performing product and design teams, champion user-centered design, guide data-driven decisions, and translate complex robotics, hardware, software, and operational capabilities into customer value. Partner with executive leadership across Engineering, Operations, Safety, Policy, and Business Development to drive commercialization and growth.

Lead performance marketing data science initiatives focused on lead segmentation, bidding optimization, targeting, and marketing spend allocation. Develop and deploy production-ready machine learning systems, establish research and ML operations roadmaps, build monitoring frameworks, and identify high-impact opportunities. Partner with engineering, marketing, vendors, and executives while mentoring data scientists and machine learning engineers and promoting rigorous, maintainable, and reproducible practices.

Build and deploy AI agents, intelligent applications, scalable backend services, APIs, and full-stack solutions. Integrate large language models, AI services, data platforms, and external APIs into customer-focused products. Collaborate with engineering, product, and cross-functional teams while contributing to cloud-native applications, data pipelines, distributed systems, testing, deployment, and production operations.

Leads AI presales strategy, customer advisory, technical solution architecture, and team development. Translates business needs into AI use cases, prototypes, business cases, roadmaps, and repeatable offerings. Partners with sales, engineering, consultants, and technology vendors to support complex pursuits, executive briefings, demonstrations, proofs of concept, and AI business growth. Promotes responsible AI governance, tracks pipeline performance, develops thought leadership, and remains hands-on in high-impact customer engagements.

Lead and manage the underwriting and credit risk model validation team, perform hands-on independent validations of complex ML models, assess data integrity and performance, identify and remediate model weaknesses, partner on MRM framework improvements, and coordinate with audit, compliance, and controls to meet regulatory standards.

Drive new enterprise revenue and customer expansion by selling AI-powered SaaS solutions. Manage complex 6–12 month sales cycles, build executive relationships, develop value propositions, maintain pipeline accuracy, lead virtual account teams, and close high-value deals. Translate technical AI capabilities into measurable business outcomes, guide customer adoption, collaborate with marketing, product, and customer success teams, and exceed quarterly pipeline and revenue targets.

Lead product vision, strategy, discovery, and Agile delivery for insurance platforms. Partner with engineering, data science, design, QA, and insurance domain teams to prioritize opportunities, manage backlogs, evaluate build-versus-buy decisions, guide technical design, coordinate delivery, and measure outcomes. The role requires strong knowledge of insurance policy lifecycles, integrations, data modeling, pricing, underwriting, and AI-enabled workflows.

Leads product strategy, roadmap prioritization, AI and automation initiatives, client engagement, and cross-functional delivery for SPINS Intelligence and Portal. The role combines hands-on product management with PM team leadership, including writing user stories, defining acceptance criteria, triaging issues, reviewing sprint output, managing product economics, and developing business cases. Requires strong CPG, SaaS analytics, data product, AI/ML, and technical platform expertise.

Own Sophos EDR strategy, roadmap, technical product development, AI-powered experiences, and go-to-market execution. Translate customer needs, threat research, telemetry, competitive intelligence, and business goals into product priorities. Partner with engineering, security operations, threat research, marketing, sales, and customer success to deliver endpoint detection, investigation, threat hunting, response, reporting, and XDR/MDR upgrade capabilities while driving adoption, revenue, and customer outcomes.

Serve as the technical sales and customer-facing expert for an AI platform. Responsibilities include conducting product demonstrations, supporting proofs of concept, advising on architecture and development, planning professional services projects, tuning retrieval and agentic pipelines, responding to RFPs, supporting customer adoption, and communicating product feedback internally. The role requires strong AI, cloud, on-premise, application development, storytelling, and enterprise customer engagement experience.

Architect and prototype scalable solutions for Twilio’s emerging technologies experimentation team. Lead AI, machine learning, and LLM-driven initiatives; guide technical direction; mentor engineers across the US and India; align prototypes with business goals; and ensure enterprise architecture, scalability, extensibility, and production readiness. The role requires extensive architecture or principal engineering experience, cloud expertise across AWS and Azure, full-stack development knowledge, database expertise, and strong technical leadership.

Annotate and label data accurately according to project guidelines, conduct quality checks, provide feedback on annotation tools and processes, collaborate with project teams, maintain efficiency, and identify issues for resolution. The role requires strong attention to detail, critical thinking, adaptability, communication skills, and comfort with repetitive computer-based work.

Lead the development of machine learning systems for Cedar Pay's Personalization Engine, manage ML engineers, and ensure ML model effectiveness.

Own the design vision and execution for experimental AI-powered learning tools co-created with teachers and researchers. Design teacher- and student-facing products, create flows, wireframes, interaction designs, and functional prototypes, and facilitate collaboration across educators, researchers, engineers, and data scientists. Conduct usability testing and validate research hypotheses while navigating ambiguity, data-heavy workflows, and uncertain AI/ML-enabled features.

Architect and scale cloud-based ML infrastructure supporting large-scale training, massive datasets, model development, and customer services. Own platform design, implementation, deployment, monitoring, reliability, cost optimization, and usability. Collaborate with data science, product, and operations teams, solve complex distributed-systems challenges, and mentor engineers while driving technical innovation.

Manage paid social campaigns and full-funnel strategies across Meta, LinkedIn, TikTok, Pinterest, Snapchat, and other paid channels. Oversee client accounts, analyze performance, identify growth opportunities, build client relationships, and collaborate with SEO, creative, and analytics teams. Contribute to thought leadership, coach associates, maintain quality standards, and explore automation, AI, and LLM search visibility initiatives.

Own the strategy, execution, customer success, and go-to-market for Oscilar’s Device and Behavior product. Lead development of SDKs, signal pipelines, behavioral biometrics, agentic identity signals, data networks, foundation-model capabilities, and analyst experiences. Partner with sales and customers to drive adoption, shape product positioning, and deliver a device intelligence and digital identity platform for fraud, onboarding, authentication, and risk decisions.

Develop and maintain pricing and predictive models for credit insurance products. Analyze claims, loss ratios, portfolio performance, profitability, credit risk, and economic scenarios. Recommend pricing actions, support forecasting and reserves, evaluate underwriting strategies, and partner with underwriting, finance, claims, data science, and technology teams. Ensure model governance, documentation, and actuarial standards compliance.