Top Machine Learning Jobs in Santa Cruz, CA

The Global Process Owner for S&OP leads the design and continuous improvement of the S&OP process, ensures collaboration across functions, and drives digital transformation and automation within the organization.

Senior Business Systems Analyst to define requirements for agent-facing servicing applications, design integrations/APIs with core banking systems, translate needs into user stories and acceptance criteria, improve agent workflows and productivity, and identify AI/ML opportunities for fraud detection, automation, and operational analytics. Supports testing, validation, and releases.

Gather and document functional requirements for agent-facing servicing applications, define integrations/APIs with core banking systems, consult with stakeholders, identify AI/ML opportunities (fraud, KYC, automation), support testing and implementation, and act as SME while managing risks and compliance.

Lead strategic GTM and co-selling initiatives with Google Cloud to drive NVIDIA AI adoption. Develop and maintain technical enablement assets, run developer activations and events, collaborate with Google Cloud field and engineering teams, convert samples into demos and labs, and translate customer feedback into product and roadmap insights.

Lead go-to-market strategy for Magnet Forensics' security, incident response, and AI product portfolio. Influence executive, product, and sales stakeholders; translate technical AI/ML and forensic capabilities into messaging; develop buyer personas; create sales enablement and demand-generation content; evangelize products at events; partner with competitive intelligence and customer advocacy; mentor product marketing team members.

Design reusable software tools and subsystems, lead implementation and estimation, ensure systems integration and multi-platform compatibility, drive projects to completion, and perform feasibility analyses for future projects.

Design and productionize ML models and agentic systems for intraday financial alpha generation. Build data pipelines, experiment frameworks, and portfolio optimization methods. Collaborate with quants and engineers to move models from research to live trading environments.

Lead strategy, roadmap, and delivery of AI-driven networking products using ML, LLMs, generative and agentic AI. Translate customer needs into product requirements, define KPIs, align cross-functional teams, drive go-to-market and thought leadership, and mentor product managers.

Independently validate and monitor machine learning models for credit underwriting, credit risk, and fraud detection. Identify model weaknesses, recommend improvements, and collaborate with model owners to remediate findings. Support and maintain the Model Risk Management framework and coordinate with Internal Audit, Internal Controls, and Compliance on audit and regulatory requests.

Lead complex transaction monitoring investigations to detect money laundering, terrorist financing, and fraud. Analyze large datasets and payment activity, coach analysts, review investigation quality, improve monitoring rules and processes, collaborate cross-functionally, and support regulatory reporting and audits.

Lead product vision and roadmap for data and analytics products (ML, generative AI, data pipelines, BI). Translate business needs into developer-ready features, maintain backlog, collaborate with engineering and stakeholders, track KPIs, and drive adoption and continuous improvement.

Build and maintain full-stack features, data pipelines, and cloud infrastructure for large-scale ad management and analytics. Integrate with advertising platform APIs, apply ML/NLP for forecasting and recommendations, collaborate with product and marketing teams, and use AI coding tools to accelerate development.

Lead causal inference and measurement efforts for payments, build and evaluate ML/AI and LLM-based models, optimize fraud mitigation and loss strategies, simulate interventions, and communicate findings to stakeholders to drive product and risk decisions.

Own end-to-end analysis of payments, chargebacks, and user lifecycle to identify drivers of net loss. Deep-dive into fraud cases, design and evaluate A/B experiments and risk-rule changes, build monitoring dashboards, partner with Risk, Payments, and Product to convert insights into mitigations, and run ad-hoc analyses of emerging loss trends.

Lead and execute independent research programs on NLP and LLM safety, design and run experiments, secure funding, publish and present results, lead cross-functional teams, and develop evaluation tools and challenges to advance digital safety.

Design and implement scalable data pipelines and ML algorithms to optimize StackAdapt's digital advertising platform. Collaborate with teams to solve complex problems.

Own end-to-end compliance issue management for Lemonade's personal lines insurance products: identify, track, prioritize, remediate, and report issues; analyze data for root causes and trends; build remediation plans; recommend risk-based priorities to leadership; engage regulators; and develop AI/automation tools and cross-functional reporting to improve compliance processes.

Create end-to-end computer-use tasks for AI agents in Linux desktop environments. Design realistic multi-application scenarios, prepare files and instructions, write Python setup and automated scoring scripts, test tasks, and document them. The contract requires daily check-ins, rapid feedback, and availability throughout a three- to four-week engagement. Experience with AI tools, AI/ML projects, Linux VMs, Git, reinforcement learning environments, or computer-use benchmarks is relevant.

Build and optimize infrastructure for Netflix’s machine learning and AI systems, including distributed training, offline and post-training platforms, GPU-optimized inference, and serving. Collaborate with modeling teams on model-system codesign and solve large-scale infrastructure challenges. The internship lasts at least 12 weeks and is intended for PhD students returning to school afterward.

Conduct applied machine learning research at Netflix across areas such as recommender systems, NLP, computer vision, causal ML, agentic AI, multimodal modeling, reliable ML, and model efficiency. Develop and train models using modern frameworks and GPUs, apply distributed training and inference, build end-to-end ML pipelines, and communicate research findings. The 12-week onsite internship is based in Los Gatos, California, beginning in January 2027.

Lead analytics partner to Community Support, driving data-driven roadmaps, scalable reporting and measurement, causal inference and experimentation, and improvements to agent/guest/host experiences via analytics, modeling, and stakeholder influence.

Design and implement scalable backend and distributed systems for a marketing intelligence platform. Build near-real-time ingestion and processing, scale an incrementality/experimentation product, integrate AI-powered workflows, improve data ingestion/exports and connection-state systems, and contribute frontend components to deliver end-to-end product experiences.

Lead a Big Data Engineering team to deliver production-grade ETL and data pipelines at scale. Own team execution, code quality, morale, and roadmap execution with product managers. Remove roadblocks, coach and develop engineers, promote engineering best practices, and enable data-driven and AI-powered product features (personalization, recommendations, search, ranking, matching).

Evaluate and strengthen AI model behavior on cybersecurity topics by writing expert prompts, annotating and grading model responses for accuracy and safety, and applying structured guidelines to classify sensitive content. Work part-time on AI safety tasks with emphasis on secure, responsible handling of dual-use information.

The AI Solutions Lead will expand Quantiphi's business in key insurance accounts by managing executive relationships, developing account strategies, leading sales initiatives, and ensuring client satisfaction.