Top Remote Machine Learning Jobs in San Diego, CA

Onboard new customers, support product deployment, and create playbooks for customer success and new-hire training. The role works closely with the CEO and CSO at a climate technology startup, addressing ambiguous problems and developing innovative solutions for agriculture and energy customers. Experience in customer success or product management is required; agriculture, SaaS, and early-stage startup experience are preferred.

Leads technical pre-sales for supply chain integration solutions, partnering with Sales to conduct discovery, design ERP/WMS/TMS and trading-partner architectures, run POCs, deliver demonstrations, and respond to RFPs. Advises technical and executive stakeholders on business outcomes and AI opportunities, translates capabilities into measurable ROI, and collaborates with Product, Customer Success, and Professional Services on roadmap feedback and post-sale handoffs.

Lead product strategy, roadmap, and delivery for Falcon Complete’s internal MDR tooling, including AI-powered investigation and triage, ticketing, case management, and analyst workbench systems. Partner with security operations, analysts, engineering, and data science teams to translate workflows into product requirements, prioritize automation, improve analyst productivity and response times, define success metrics, and enhance customer outcomes.

Own the strategy, architecture, development, deployment, monitoring, and continuous improvement of production personalization and recommendation systems. Build custom embeddings, ranking models, and shared representations across feeds, search, and recommendations. Lead ambiguous problems from concept to measurable customer impact, establish AI evaluation standards, partner with product and data teams, make cross-team technical decisions, and coach senior engineers.

Designs and prototypes AI-driven solutions using Cisco and Splunk technologies for strategic public-sector customers. The role partners with customers and internal product, engineering, sales, and technology teams to accelerate business outcomes, influence technical decisions, and deliver proofs of concept across complex enterprise environments. Responsibilities include architecture, customer engagement, AI implementation, technical storytelling, mentoring other forward-deployed engineers, and sharing field insights to improve products. Travel within the assigned territory is required.

Conduct research at the intersection of AI, cybersecurity, and computer systems. Formulate research questions, design experiments, build prototypes, evaluate AI and security methods, analyze real-world security data, and communicate findings through reports, presentations, and potential publications. The role combines research with practical engineering and offers mentorship while developing independence.

Leads Payment Integrity strategy and audit optimization by identifying claims-payment anomalies, reimbursement trends, provider behaviors, and new data-mining opportunities. Develops and tests audit concepts, evaluates financial and operational impact, and guides successful concepts through implementation across prepay and postpay processes. Optimizes existing audits, influences cross-functional stakeholders without direct authority, provides executive recommendations, and supports sustainable medical cost savings.

Leads Humana’s enterprise digital platform portfolio, defining product vision, investment priorities, adoption strategies, governance, and value-realization metrics. Oversees platforms including Adobe Experience Platform, digital health services, consent management, content capabilities, and the enterprise design system. Partners with business, design, technology, and engineering leaders to align roadmaps, drive reuse and accessibility, manage dependencies, and improve customer and business outcomes. Develops senior product leaders and cross-functional teams while guiding executive investment decisions in a regulated environment.

Build and evolve machine learning tooling and infrastructure for AI-powered clinical solutions, including automated prompt tuning, feedback loops, model benchmarking, experimentation, and evaluation. Partner with data science, clinical, software engineering, and product teams to develop scalable solutions, solve ambiguous technical problems, establish engineering standards, and provide technical mentorship without direct management responsibility.

Leads the enterprise Data Science and AI vision, strategy, roadmap, and global organization. Oversees production-grade machine learning, Generative AI, Agentic AI, predictive modeling, AI governance, deployment, monitoring, cost optimization, and responsible AI. Partners with executive, product, engineering, commercial, legal, operations, and customer success stakeholders to deliver scalable AI products, automation, improved customer outcomes, and measurable business value.

Designs and scales AI-driven security GRC automation, including compliance evidence collection, control testing, risk assessment, audit preparation, and workflow integration. Builds scripts, bots, APIs, and AI/LLM solutions; integrates GRC platforms with security and IT systems; establishes quality controls and guardrails; monitors automation risk and performance; and partners with technical and business stakeholders to improve security compliance maturity.

Build scalable systems and machine learning models that transform satellite imagery into labeled geospatial data and production computer vision products. Responsibilities include developing data and annotation pipelines, tracking lineage and versioning, deploying inference services, evaluating models, supporting cloud infrastructure, and improving labeling workflows. The role requires strong Python, cloud, containerization, CI/CD, infrastructure-as-code, production support, and deep learning expertise, with preferred experience in GCP, Vertex AI, spatial databases, and satellite imagery.

Own and scale Jellyfish’s security engineering program, including strategy, architecture, secure SDLC governance, AI security, threat modeling, vulnerability remediation, incident response, penetration testing, and bug bounty programs. The role combines hands-on software development with executive advising, cross-functional leadership, mentorship, and security risk management across SaaS infrastructure and customer-facing AI products.

Drive product and business impact through statistical modeling, experiment design, Bayesian marketing analysis, machine learning, NLP, LLM applications, and classifier implementation. Build growth metrics, monitoring pipelines, evaluation datasets, and AI trigger systems across search and Duck.ai. Analyze privacy-constrained data, deploy production-grade models, optimize SQL queries, and lead complex projects from scoping through production with minimal direction.

Lead product strategy for AI-driven business automation and a monetized data-as-a-service offering. Build enterprise AI governance, agent operating models, evaluation standards, privacy controls, and responsible AI practices. Develop an AI champion network, prioritize automation opportunities, and measure productivity gains. Define data products, customer segments, pricing, packaging, and go-to-market plans while partnering across engineering, security, legal, privacy, risk, and business units.

Own post-sale relationships with referral partners, driving adoption, retention, renewals, expansion, and revenue growth. Serve as a strategic advisor, lead business reviews, resolve escalations, coordinate cross-functional teams, enable product usage, and advocate for partner needs. Maintain partner records and report on satisfaction, renewal, and revenue metrics while developing success strategies and playbooks.

Own the strategy, roadmap, requirements, launch, adoption, and success metrics for a technical platform. Partner with engineering, data, product, and business teams; evaluate architecture, APIs, and system tradeoffs; prioritize foundational investments against customer and revenue outcomes; and apply AI to improve platform capabilities. The role requires strong product management experience, technical depth, analytical skills, and production experience with LLM-based or ML-adjacent products.

Provides senior-level presales technical consulting for complex networking and security solutions. Partners with sales teams and customers to design WAN, data center, cloud, and security architectures; develops proposals, bills of materials, and implementation specifications; delivers technical presentations, demonstrations, proofs of concept, and training; and influences executive technology decisions. Requires extensive Juniper and networking expertise, customer-facing sales experience, and the ability to travel as business demands require.

Leads talent acquisition strategy for specialized AI, machine learning, robotics, automation, and advanced research roles. Sources and closes highly technical candidates, advises senior leaders on talent markets and compensation, mentors recruiters, builds research and industry relationships, and improves recruiting processes using analytics, recruiting technology, and Lean methods.

Leads end-to-end data architecture, engineering pipelines, semantic-layer development, data quality, monitoring, and platform operations. Manages sprint planning, releases, DevOps metrics, project delivery, and stakeholder communication. Establishes MLOps and machine-learning platform capabilities, mentors data engineers, and ensures reliable, well-documented data solutions that support organizational reporting and analytics needs.

Manages a business analytics team that delivers insights, KPI reporting, trend analysis, data visualizations, governance, and actionable recommendations. Guides statistical analysis, data modeling, querying, experimentation, and self-service analytics while aligning practices with enterprise standards. Partners with leaders to identify opportunities and risks, improve data quality, drive analytics adoption, and support strategic and operational decisions. Oversees team performance, department projects, communications, and best-practice development.

Lead the design and development of Azure Compute Fleet capabilities that orchestrate Virtual Machine Scale Sets across regions. Build distributed control-plane services, APIs, and workflows for capacity placement, scaling, failover, and lifecycle management. Collaborate across Azure engineering teams, drive AI-enabled engineering improvements, participate in on-call operations, and improve reliability, observability, performance, and recovery for hyperscale cloud services.

Leads global people analytics and workforce intelligence strategy, building AI-driven tools, predictive models, dashboards, and decision-support applications. Partners with IT, data, and security teams on governance, privacy, and ethical AI. Advises executives, democratizes workforce insights, measures people-program ROI, and anticipates talent trends through benchmarking and workforce modeling.

The AI Engineer designs, develops, and operationalizes responsible AI systems, including generative AI applications, RLHF models, guardrails, prompt engineering, and RAG frameworks. Responsibilities include evaluating and fine-tuning models, developing Python applications on cloud platforms, implementing ML software engineering practices and CI/CD pipelines, and collaborating with stakeholders on AI roadmaps and delivery. The role requires leading complex AI/ML initiatives and translating technical concepts for diverse audiences.

Develop and validate machine learning and predictive models, perform statistical and data analysis, conduct ETL across repositories, and assess model quality. Communate findings through visualizations and written reports, automate analytical workflows, and collaborate directly with nontechnical customers to refine requirements and deliver data-driven solutions. The role requires expertise in ML/AI, programming, cloud environments, CI/CD, and government or safety-critical systems.