Top Machine Learning Jobs in San Diego, CA

Leads client-facing applied AI engineering engagements in healthcare, from solution design through deployment. Translates clinical and health services needs into AI requirements, architectures, and roadmaps; designs RAG, memory, prompt, and agent orchestration components; guides data, model integration, and cloud implementation; evaluates model performance; advises on AI platforms and governance; and develops LLMOps and AgentOps practices. Coaches teams, resolves delivery challenges, and communicates technical decisions to clients and leaders.

Supports client cloud modernization engagements by designing and building Google Cloud data and generative AI solutions. Develops Python workflows for prompt engineering, retrieval-augmented generation, agent orchestration, and LLM evaluation. Configures Vertex AI, LLM APIs, vector databases, guardrails, and LLMOps processes while applying machine learning and NLP concepts. Collaborates on data platform and analytics modernization, troubleshooting, research, solution design, documentation, and iterative improvement with senior staff.

Leads AI-enabled analysis and strategy for an oncology research portfolio spanning discovery through preclinical programs. Uses predictive modeling, scenario analysis, knowledge graphs, competitive intelligence, and advanced analytics to assess scientific strength, probability of success, portfolio fit, prioritization, and resource allocation. Collaborates with research, translational, clinical, and strategy teams, presents recommendations to senior leadership and governance committees, and champions AI-driven decision-making across pharmaceutical R&D.

Build and deploy production-grade AI and machine learning systems supporting biomedical research and drug discovery. Own technical solutions from architecture through deployment, including data pipelines, retrieval systems, knowledge systems, inference services, evaluation frameworks, and human-in-the-loop workflows. Partner with scientists and engineers to transform prototypes into scalable, secure, reliable platforms. Lead technical delivery, make architecture tradeoffs, improve engineering standards, mentor teammates, and promote responsible AI practices.

Develop and deploy data solutions using Palantir Foundry and AIP for client engagements. Lead technical teams, coach team members, analyze system interactions, build applications with large datasets, and apply data engineering and analytics to deliver client outcomes. The role also includes managing client relationships, planning and executing projects, encouraging technology adoption, resolving stakeholder conflicts, and driving continuous improvement.

Conduct computational and translational analyses across biological, molecular, genetic, clinical, and translational datasets. Translate scientific questions into analytical workflows, generate biological insights, evaluate uncertainty, and communicate findings to multidisciplinary stakeholders. The role supports target identification, biomarker discovery, patient stratification, disease biology, drug discovery, and clinical development while building expertise in AI-enabled scientific workflows and pharmaceutical research.

Provides principal-level ServiceNow consulting, architecture, engineering, and delivery oversight for enterprise clients. Leads technical solution design, implementations, automation, cloud deployment, presales estimation, workshops, and client advisory activities. Establishes technical standards, mentors engineers and architects, supports governance and practice growth, and serves as an escalation point. The role also develops AI-enabled ServiceNow offerings and requires approximately 10% travel.

Build and tune fraud detections across accounts, authentication, payments, and other high-risk flows. Lead threat hunts, identify emerging abuse patterns, improve fraud telemetry, and convert findings into automated, tested detections. Develop tools, dashboards, and enrichment for fraud operations; measure precision, recall, coverage, and detection speed; and collaborate with security, engineering, risk, product, and operations teams to address systemic fraud risks.

Own Treasury’s data architecture and technical roadmap, personally building and operating a Snowflake liquidity datamart with source integrations, data models, reconciliation, controls, and data contracts. Partner with machine learning engineers on forecasting datasets and lead the Treasury Management System build-versus-buy recommendation. The role requires production reliability, automation, incident response, stakeholder management, and translating ambiguous financial business problems into adopted systems.

Develop and lead complex AI and data science solutions, shape AI technical strategy, research emerging technologies, resolve large-scale technical problems, and guide cross-functional teams. The role focuses on scalable data solutions, machine learning, deep learning, large language models, generative AI, NLP, computer vision, Python, and Vertex AI. It requires a bachelor's degree and at least five years of progressive data analytics and data science experience.

Lead teams building Samsara Assistant, Agent Studio, and agent extensibility. Own product growth, activation, answer quality, trust, repeat usage, and proactive agent capabilities. Set technical direction for an AI platform used by customers and internal product teams, partner across Safety and Maintenance, and lead approximately 10 machine learning, applied science, and full-stack engineers through two tech leads. Hire, develop, and manage a high-performing engineering organization.

Lead and transform the Fraud Intelligence Team into a high-impact function by improving operating models, QA, tooling, and metrics; build entry-level talent programs; connect labeling to measurable fraud prevention outcomes; deliver actionable insights to Product, R&D, Analytics, and GTM; and leverage AI and automation to increase productivity and business impact.

Lead product strategy, roadmap, and execution for Zscaler’s threat detection and response offerings within its Agentic SOC and MDR business. Translate threat intelligence and customer needs into innovative security products, coordinate engineering and go-to-market teams, and measure product impact. The role owns commercialization, technical direction, product quality, and business outcomes while requiring deep cybersecurity expertise and experience with enterprise security products.

Lead Amazon performance marketing strategy and execution, manage Sponsored Ads and DSP, collaborate with agencies and cross-functional teams, optimize campaigns and budgets, analyze performance with AMC/analytics, and measure commercial impact to drive ROAS and customer acquisition.

Leads strategy, roadmap, investment, and execution for horizontal AI capabilities, shared AI platforms, and AI-native cybersecurity products. Translates AI research into reliable, secure, scalable customer solutions; drives adoption across product teams; establishes launch and autonomy criteria; partners with customers and cross-functional leaders on commercialization; and builds a high-performing AI product organization.

Leads complex, cross-functional technical programs for the Superintelligence Lab. Responsibilities include roadmap execution, milestone and dependency management, risk mitigation, issue resolution, stakeholder alignment, leadership reporting, and implementing program-management best practices. The role requires collaboration with engineering, data science, and leadership teams while using AI technologies and Jira to improve workflows, decision-making, and business outcomes.

Investigate complex AML cases, review transaction-monitoring alerts, determine SAR/STR filing decisions, and communicate findings to regulators, law enforcement, and stakeholders. The role also supports audits, improves AML detection models and typology coverage, analyzes transactional data, identifies financial crime trends, and provides analyst training. Candidates need strong blockchain, AML regulations, investigative writing, and blockchain forensic tool experience, with AI and SQL capabilities preferred or advantageous.

Lead firmware and embedded software development for AI-powered video safety products. Architect scalable embedded systems, integrate camera and real-time edge computing capabilities, deploy CV/ML models, and guide technical decisions. Collaborate with hardware, cloud, machine learning, and product teams on next-generation devices and complex multi-team projects. Maintain production systems, improve testing and release infrastructure, mentor engineers, and support products from development through mass production.

Architects and operates enterprise cryptographic services for a regulated, multi-tenant SaaS platform. Responsibilities include building FIPS-compliant key management, PKI, secrets, and workload identity systems; running Kubernetes services; threat modeling; evaluating AI/ML for security applications; collaborating on compliance requirements; mentoring engineers; and advancing post-quantum readiness.

Leads the Safety AI organization and architecture for on-device and cloud-based computer vision and machine learning systems. Oversees sensor fusion, model lifecycle management, platform architecture, experimentation, deployment, monitoring, and continuous improvement across more than two million dashcams. Partners with product, firmware, full-stack, and platform teams to deliver reliable, low-latency safety features. Builds and develops teams of applied scientists and engineers while driving measurable improvements in driver safety and customer outcomes.

Lead the design and implementation of AI initiatives for edge devices. Develop and optimize machine learning solutions using large-scale sensor, video, text, diagnostic, transaction, and location data. Partner with engineering and business teams to prototype AI experiences, ship production systems, adopt research advances, and improve edge model performance. Mentor ML engineers and communicate technical concepts to varied audiences.

Own the full B2B sales cycle across EMEA and the US, from outbound prospecting and opportunity creation through negotiation, closing, and account expansion. Sell content syndication, qualified leads, and intent-led demand-generation programs to senior marketing and sales decision-makers. Build pipeline, meet revenue targets, maintain accurate CRM forecasts, grow existing accounts, and collaborate with delivery, data, media, and client success teams.

Own the full sales cycle for enterprise B2B demand-generation clients across EMEA and the US. Responsibilities include prospecting, building outbound pipelines, developing account strategies, engaging executive buyers, negotiating complex contracts, closing net-new business, expanding accounts, forecasting accurately, and collaborating with delivery, data, media, marketing, and client success teams.

Leads a multidisciplinary data science and data engineering team supporting Finance. Sets strategy, prioritizes initiatives, guides analytical models and data architecture, advances AI and machine learning applications, improves data quality and reliability, establishes scalable operating processes, and communicates technical recommendations to Finance and executive stakeholders. The role also manages talent, oversees forecasting and decision-support outcomes, and drives measurable business impact.

Leads GitLab’s Product, Sales, and Finance Data Science teams, managing managers and a 12-person organization. Sets data science standards, prioritizes investments, partners with executives, drives product adoption and revenue growth, and oversees experimentation, causal inference, predictive modeling, and AI initiatives. Develops talent, builds succession, and collaborates with Analytics Engineering and Data Engineering on a shared data and AI roadmap.