Top Remote Machine Learning Jobs in Santa Cruz, CA

Develops and deploys machine learning systems for high-stakes decisions in pricing, claims, underwriting, and predictive health. Responsibilities span problem framing, quantitative research, experimentation, causal inference, predictive modeling, multimodal and foundation model development, agent systems, production deployment, and ongoing iteration. The role requires strong mathematical and statistical foundations, Python fluency, and expertise in machine learning, statistics, econometrics, or causal inference.

Build and deploy computer vision and machine learning solutions for power grid infrastructure, including defect detection, thermal anomalies, vegetation monitoring, and intrusion surveillance. Responsibilities span research implementation, model experimentation, error analysis, data pipelines, experiment tracking, model serving, testing, monitoring, and client delivery. The role requires productionizing models, adapting research papers, optimizing architectures, and independently owning projects from problem definition through deployment.

Own end-to-end computer vision and machine learning projects for power grid infrastructure, from problem framing and research experimentation through production deployment and monitoring. Develop detection, segmentation, classification, anomaly detection, and foundation-model solutions; build data pipelines, serving systems, experiment tracking, and model versioning. Conduct error analysis, benchmarking, tuning, code reviews, and testing while translating client requirements into reliable production models and communicating technical decisions and limitations.

Build and improve machine learning systems for ad ranking, relevance, and optimization. Responsibilities include developing active learning and LLM-assisted labeling workflows, owning experimentation and evaluation, and deploying low-latency inference services. The role partners with product, data, and platform teams to improve advertiser performance and user engagement while ensuring production reliability, scalability, latency, and data quality. Candidates will use Python, Go, AWS, distributed systems, and AI-assisted development tools.

Lead the development of deep learning models for email data, optimize models, design evaluation frameworks, integrate products, and mentor junior researchers.

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 onboarding and training for new customers of an AI-powered construction SaaS. Customize training, run webinars and office hours, create documentation, drive user adoption, surface issues with support, and collaborate with product and customer success to refine onboarding processes.

Lead strategy, roadmap, and delivery for Tekmetric's SaaS product for auto repair shops. Own user research, AI/ML product initiatives, cross-functional alignment, go-to-market coordination, and measurement of business outcomes. Train internal teams and iterate on features from concept through launch and optimization.

Lead design and production deployment of ML models focused on pricing, availability, forecasting, and optimization. Build data infrastructure, governance, testing/monitoring, and dashboards. Partner with executives and cross-functional teams, mentor others, and establish scalable data-science practices and experimentation frameworks.

Build and deploy low-latency machine learning models and inference infrastructure for Netflix’s advertising platform. Develop identity resolution, behavioral and contextual targeting, lookalike expansion, user signals, and audience features at massive scale. Collaborate with science, product, engineering, operations, design, and research teams to productionize models while supporting privacy, governance, security, and advertiser brand safety.

Develop, optimize, and deploy scalable predictive ML models to inform content strategy, valuation, scheduling, and performance. Own end-to-end model lifecycle (feature engineering, training, evaluation, deployment, monitoring) and partner with analytics, data engineering, MLOps, and content teams to scale production solutions and influence ML infrastructure.

Lead and grow a team of Conversation Designers to build and scale conversational AI experiences. Partner with Product, Engineering, Analytics, and ML teams to drive strategy, tooling, evaluation, and quality practices. Recruit and remove roadblocks, own strategic initiatives, and remain hands-on in designing, testing, and improving AI-driven conversational products.

Leads the architecture, research, development, evaluation, and production deployment of GenAI and LLM-based healthcare ML solutions. Works with healthcare data, cloud platforms, data engineering, and software teams to deliver SaaS-scale systems. Establishes responsible AI practices, designs RAG and NLP solutions, advises clients, leads technical discussions, mentors teams, manages large projects, and translates technical insights into measurable business outcomes.

Lead full sales cycle to drive CTV performance advertising growth in North America: prospect, close, launch and scale CTV budgets, manage key advertiser relationships, collaborate cross-functionally, represent Jampp at events (20% travel), and provide local feedback to improve global initiatives.

Senior Business Systems Analyst responsible for capturing functional requirements for agent-facing servicing applications, defining integrations/APIs with core banking systems, translating needs into user stories and acceptance criteria, supporting testing and releases, analyzing workflows to improve agent productivity, and identifying AI/ML opportunities for fraud detection and automation.

Lead development and optimization of web scraping pipelines to extract, clean, and store large-scale web data. Handle dynamic content, pagination, distributed scraping, database design with NoSQL, deploy jobs to cloud, and monitor systems for reliability and data quality. Support ML-based data cleaning and categorization.

Lead end-to-end enterprise customer engagements and onboarding for large insurance clients, run program timelines and executive reporting, design and deploy AI agents and internal tooling, own the CE operations roadmap and knowledge base, drive cross-functional resolution of technical/operational issues, and continuously improve efficiency and quality across the Client Engagement team.

Lead global marketing strategy and GTM to scale Aisle into a market leader. Own brand, PR, product positioning, full-funnel demand generation, ABM, marketing tech stack, RevOps, attribution, budget/agency management, and grow a multidisciplinary marketing team aligned to revenue targets.

Lead product strategy and execution for data and AI platforms, driving GenAI/LLM initiatives, cross-functional collaboration, responsible AI governance, experimentation (A/B testing), measurement of model impact, and mentoring product teams to embed scalable ML capabilities into clinical and member experiences.

Own pre-sales technical engagement: consult customers, deliver technical presentations and demos (web, iOS, Android), lead POCs, support RFP/RFI responses, manage onboarding and integrations, and advise sales, product, and engineering teams on implementation and value realization.

Design, build, and scale backend systems and APIs for high-transaction consumer products. Collaborate with product, credit, compliance, support, and analytics teams. Contribute to code reviews, technical planning, security, and operational excellence. Mentor peers and promote engineering best practices.

Lead the development and implementation of machine learning solutions, managing a team to drive strategic decisions and innovative business outcomes.

Design, implement, and scale ML-based solutions for search and advertising; define data collection, evaluation, and production serving strategies; analyze data to inform development; communicate cross-functionally; produce technical reports; and stay current with advances in LLMs, vision models, and generative AI.

Leads the end-to-end execution layer for machine learning, including data pipelines, large-model training and fine-tuning, evaluation, inference, deployment, monitoring, and reliability. Designs scalable GPU-based systems, optimizes performance and cost, partners with research and application engineering, and translates model capabilities into measurable production improvements. Also provides technical leadership, judgment, and support for high-impact ML delivery.

Lead end-to-end development of an AI-driven decision-support system for multi-sensor data. Responsibilities include building data ingestion and fusion pipelines, training and deploying supervised and unsupervised ML models, optimizing near-real-time inference, creating GPU-accelerated dashboards, integrating modular containerized systems, and supporting secure deployment and testing. The role works directly with the founder, owns technical delivery autonomously, and collaborates with subcontractors on human factors, model validation, and security auditing.