Top Machine Learning Jobs in Santa Cruz, CA

Lead development and deployment of large-scale LLM and GenAI solutions for cybersecurity. Drive research strategy, model training, evaluation, safety/red-teaming, and cross-functional productization. Mentor data scientists, set technical standards, and represent the team through publications and thought leadership.

As a Staff Machine Learning Engineer, you will design AI products on Edge devices, optimize ML model performance, and work with large-scale datasets, collaborating across teams to deliver impactful solutions.

As an ML Ops Infrastructure Engineer, you will design CI/CD pipelines for ML, maintain deployment systems, and implement monitoring while collaborating with research teams to ensure model quality and performance.

As a Systems Architect, you'll define and drive the infrastructure architecture for AI/ML workloads, design multi-cloud strategies, manage compute orchestration and storage systems, and lead capacity planning, while also collaborating with engineering leadership.

The engineer will build and operate AI/ML infrastructure, managing services on AWS and bare metal, using tools like Kubernetes and Terraform.

Source, engage, and qualify technical candidates using LinkedIn, GitHub, X‑Ray, and other tools. Build pipelines for engineering and technical leadership roles, run outreach campaigns, perform initial screening, document candidate details in ATS/CRM, and partner with recruiters to meet client hiring timelines while improving sourcing strategies.

Lead product analytics to improve user growth, engagement, retention, and revenue. Design and analyze A/B and multivariate experiments, perform funnel/cohort analyses, maintain KPIs and dashboards, ensure event tracking, leverage ML and AI tools for advanced analyses, and translate insights into product recommendations across cross-functional teams.

Lead and mentor a software engineering team building the intake and authentication systems that feed a global Item Master. Partner with Product and AI/ML to integrate computer vision, automated tagging, and robotics into warehouse workflows. Own architecture, scalability, backend services, database design, and vendor/hardware integrations while driving agile delivery and operational OKRs.

Lead design and scaling of a unified fraud and risk platform. Define product strategy, own fraud metrics, run SQL-driven analysis and A/B tests, and partner with engineers and data teams to build real-time detection, identity verification, and transaction monitoring. Balance customer experience with risk mitigation and communicate impact to executives, legal, and partners.

The AI/Machine Learning Specialist designs and implements AI solutions, collaborates with cross-functional teams, and continuously improves ML processes.

Design and lead end-to-end AI solution architectures, ensuring scalability, security, and compliance. Translate business and regulatory requirements into technical designs, evaluate tools/platforms, document architecture standards, coordinate technical teams, and support deployment and ongoing improvements.

Lead complex, cross-functional AI programs end-to-end: translate strategy into integrated plans, drive multi-team execution, mitigate risks (data, model, compliance), apply ML lifecycle practices, use Agile/kanban methods, manage budgets/vendors, communicate to senior stakeholders, and mentor other program managers to improve AI program standards.

Lead vision, strategy, and execution for Alkami's marketing products. Define roadmap, embed AI-driven personalization and audience intelligence, partner cross-functionally to drive adoption and revenue, recruit and develop product managers, and deliver scalable SaaS marketing and analytics solutions for financial institutions.

Design and implement machine learning models to solve business problems, analyze data for insights, and optimize model performance.

Build and maintain backend APIs and data pipelines that ingest and process large-scale construction data (360° video, schedules, IoT). Optimize storage and query performance using DuckDB and PostgreSQL, extend Airflow workflows, integrate Wasm for local computation, monitor data quality, and collaborate with frontend, CV, and ML teams to deploy production models and analytics.

Lead technical pre-sales for large Oil, Gas & Energy accounts, define and implement Databricks-based data and ML/AI strategies, build technical champions, mentor teammates, and drive enterprise adoption through technical leadership and value selling.

Pre-sales Solutions Architect who defines technical strategy and drives Databricks adoption across large enterprise accounts. Leads technical engagements, builds executive relationships and internal champions, mentors team members, and maps ML/AI use cases to Databricks solutions. Supports proofs-of-concept and guides production implementations.

Lead architecture and technical strategy for large Azure modernization and AI programs. Define reference architectures, drive cross-team execution, promote AI-augmented practices, mentor architects, guide discovery and delivery, ensure non-functional requirements (security, reliability, observability) are met, and create reusable assets to scale enterprise AI/ML adoption and platform modernization.

Own a scoped project in machine learning, AI infrastructure, MLOps, data, robotics, simulation, or world models. Work with production repositories, clusters, and data to deliver a lasting artifact such as a dataset, benchmark, evaluation, or tool, supported by a mentor and technical write-up.

Designs enterprise AI/ML architectures, reference architectures, MLOps standards, lifecycle controls, and technical playbooks for the Veterans Health Administration. Provides consultation to AI pilot teams, evaluates vendor solutions, supports AI catalog and center-of-excellence planning, and translates federal security, authorization, interoperability, and risk requirements into executable architecture decisions. The role is remote and requires collaboration with program leaders, engineers, vendors, and VA IT teams.

Lead statistical modeling, AI/ML evaluation, data governance, data quality assurance, and executive analytics for VA EHR modernization. Build reproducible R-based analytics, reporting pipelines, dashboards, and governed data products using R, SQL, Python, Git, and cloud platforms. Evaluate model reliability, bias, drift, and operational value; integrate complex healthcare data; define metrics and validation rules; and advise stakeholders on auditable analytical practices.

Designs enterprise AI/ML architecture and product-management pipelines across the model lifecycle. Establishes MLOps standards, governance, risk management, monitoring, explainability, and compliance practices for responsible AI deployment at the VA. Advises governance bodies and senior leadership, evaluates tooling and platforms, supports CI/CD integration, and collaborates with engineering, data science, and product teams on scalable ML systems.

Manages client relationships and technology and analytics project delivery. Advises data science and engineering teams on business analysis, technology approaches, project scope, schedules, costs, risks, and quality assurance. Develops client engagement strategies, technology roadmaps, proposals, statements of work, and pricing. Partners with executives to identify portfolio growth opportunities and communicates the business value of analytics, statistics, and machine learning.

Designs and operates production multi-agent AI systems, LLM workflows, scoring and optimization algorithms, and closed-loop autonomous processes. Responsibilities include AI model evaluation, production monitoring, governance, third-party AI integration, federated data retrieval, and identifying patentable inventions. The role requires deep machine learning expertise, algorithm development, and deployment experience across complex operational environments.

Leads Mercury’s cross-line AI/ML research capability across insurance products. Responsibilities include setting strategy, building a research tooling team, modernizing workflows, developing model monitoring and AI infrastructure, establishing responsible AI guardrails, partnering across technology and data science teams, driving adoption, and coaching talent. The role requires strong quantitative, insurance analytics, Python, machine learning, generative AI, and LLM evaluation expertise, along with substantial people and cross-functional leadership experience.