Top Machine Learning Jobs in Santa Cruz, CA

Owns the strategy, roadmap, development, and adoption of financial data products and APIs. Partners with data science and engineering to integrate predictive and machine-learning models into scalable, trustworthy capabilities. Defines quality metrics, requirements, documentation, and support models while ensuring privacy, security, regulatory compliance, and downstream adoption across consumer and business banking products.

Manage and grow NVIDIA partner relationships in Manufacturing and Automotive: develop business plans, drive ecosystem enablement, increase adoption of NVIDIA AI solutions, maintain executive relationships, and present results in QBRs.

PhD-level 24-week internship to research Gaussian Splatting for streaming: explore compression strategies and datasets, quantify size/time/quality trade-offs, reduce training/encoding time, and build a proof-of-concept GS-based renderer.

Design and build large-scale ad tech systems across inventory, serving, measurement, forecasting, identity, and campaign management. Drive architecture, set technical direction, collaborate with ad operations and cross-functional partners, and deliver high-performance, privacy-safe cloud services and data pipelines for Netflix Ads.

Design, build, and productionize low-latency ML models and inference infrastructure for ad serving, inventory forecasting, yield optimization, bidding and allocation. Handle massive data with Spark, build scalable simulation systems, collaborate with product, science, and engineering to deploy models at scale, and ensure advertiser outcomes and member experience across client and server integrations.

Build and operate large-scale Ads Identity Graph and Identity Resolution systems (deterministic and probabilistic) in batch and near-real-time. Integrate identifier collection flows, DSPs, programmatic platforms, and data clean rooms while enforcing privacy, consent, and data governance. Handle extremely large datasets with big-data tools like Spark and design high-throughput, low-latency distributed systems. Collaborate cross-functionally on ad-targeting, measurement, and privacy-preserving solutions.

Design, implement, and deploy large-scale machine learning and optimization algorithms for ad ranking. Build and evaluate models, create online/offline evaluation frameworks, partner with product to define objectives and constraints, and communicate results to stakeholders to drive ad quality and performance.

Lead personalization product strategy and execution, partnering with ML scientists, engineers, designers, and research to improve recommendations. Define roadmap, prioritize experiments and evals, deliver consumer-facing ML products, and drive business impact across cross-functional teams.

Lead a team of ML engineers and researchers to build next-generation content personalization and recommendation algorithms across movies, games, and live events. Define vision and strategy, drive projects end-to-end from research to production and A/B tests, collaborate cross-functionally, hire and mentor, and improve systems using transfer learning, ranking, bandits, and deep learning.

Design, build, and evaluate large-scale ML-driven bidding algorithms for ad optimization (clicks, conversions, CPA, ROAS). Develop online/offline evaluation frameworks, inform auction/pricing design, partner with product, and communicate results to technical and non-technical stakeholders.

Design, develop, and maintain high-performance, secure Java backend services and APIs. Ensure reliability, scalability, and cost-efficiency across microservices, databases, messaging systems, and capacity planning. Troubleshoot, profile, benchmark, and optimize systems (including ML applications) and collaborate with Kazakhstan stakeholders.

Design, develop, and validate functionally safe software for robotic perception systems. Build safety-critical architecture across Linux and real-time operating systems, create testing and validation frameworks for edge cases, collaborate with machine learning, hardware, and core software teams, and mentor junior engineers on safety-critical coding practices.

Leads pre-sales consulting for strategic Big Tech and AI customers, including discovery, solution design, demonstrations, architecture discussions, proof-of-concepts, executive presentations, and technical sales deliverables. Partners with Sales, Product, Engineering, Data Science, and post-sales teams to address identity, fraud, risk, compliance, and data requirements. Translates complex technical and analytical concepts into business recommendations, develops technical win strategies, mentors teammates, and ensures effective implementation handoffs.

Drive MSSP partner growth across AMER by recruiting, onboarding, activating, and expanding strategic managed security service provider relationships. Develop territory recruitment plans, executive partnerships, joint business plans, and indirect routes to market. Help partners launch managed security services, generate pipeline, increase revenue, and accelerate time-to-revenue. Collaborate with sales, channel, marketing, services, and technical teams while managing forecasts, opportunities, executive reviews, and revenue objectives.

Develop and deploy mineral exploration instruments, sensor systems, data acquisition methods, and proprietary software. Integrate geospatial, geophysical, geochemical, geological, and geographic data; build statistically rigorous and physics-based models; identify ore-forming anomalies; and generate 2D and 3D exploration predictions. Collaborate with data scientists, engineers, geoscientists, vendors, and external partners to design field programs, deploy sensors, reduce uncertainty, and guide exploration decisions.

The Machine Learning Engineer will build data-intensive products, deploy ML models, analyze datasets, and collaborate with product teams to enhance Nextdoor's platform.

Develop and operate machine learning systems for automated manufacturing. Responsibilities include building scalable data and model infrastructure, automating ML pipelines, deploying models to production and edge devices, monitoring performance, optimizing latency, integrating AI/ML tools, managing incidents, ensuring security and privacy, and collaborating with automation engineers, data scientists, data engineers, and software teams.

Provides strategic legal counsel on clinical trials, commercial biopharma services, healthcare regulations, privacy, data protection, cybersecurity, and EU employment law. Advises on clinical documentation, data transfers, privacy assessments, AI and digital health initiatives, workforce matters, contracts, vendor agreements, and regulatory compliance. Partners with senior leadership, business teams, HR, compliance, privacy, clinical, technology, and outside counsel to manage risk and support compliant operations.

Lead and scale MEDHOST’s software engineering organization, overseeing product delivery, modernization, security, reliability, scalability, and operational performance. Develop technology strategy and architecture roadmaps while driving AI-first transformation across products, engineering workflows, and enterprise operations. Establish performance metrics, govern technology investments, manage strategic initiatives, and advise executives on emerging technologies, risks, and opportunities. The role also requires implementing generative AI, machine learning, and AI governance within healthcare software environments.

Own and grow a Southeast or Mid-Atlantic commercial territory targeting companies with 500–2,500 employees. Responsibilities include prospecting, pipeline development, new business acquisition, upselling and cross-selling, multi-product deal closure, forecasting, territory management, executive presentations, negotiation, industry events, and collaboration with internal and channel teams. The role requires communicating cybersecurity and identity-access solutions to business leaders while consistently achieving sales quotas.

Lead AI-driven localization product initiatives from concept through production. Build strategic roadmaps, incubate machine-learning capabilities, partner with researchers and engineers, and deliver scalable tools for creators and content operations. Translate user needs into impactful products, communicate strategy to executives and nontechnical stakeholders, and use data, experimentation, and ML evaluation to prioritize work and measure outcomes.

Lead technical strategy for customer accounts by designing scalable data, AI, machine learning, streaming, and real-time analytics solutions. Conduct architecture discussions, whiteboarding, design reviews, and live coding; advise senior technical stakeholders; drive Databricks adoption and consumption; coordinate cross-functional resources; demonstrate solutions in competitive situations; and provide product feedback. Requires strong Python and SQL skills, public-cloud deployment experience, and expertise in distributed data systems.

Designs, builds, scales, and operates distributed CI/CD services supporting millions of builds, tests, and releases. Develops code for Azure-based infrastructure spanning virtual machines, containers, and custom lab environments. Responsibilities include requirements analysis, architecture and design documentation, estimation, debugging, optimization, operational playbooks, on-call incident response, monitoring, reliability improvements, and mentoring engineers.

Architect and operate North’s AWS and multi-cloud infrastructure, establishing IaC, CI/CD, security, observability, and disaster-recovery standards. Build AIOps capabilities including anomaly detection, predictive scaling, self-healing remediation, AI-assisted tooling, and cloud cost optimization. Collaborate with DevOps, SRE, Product Engineering, and InfoSec teams to deliver resilient, compliant, highly scalable platforms.

Owns the end-to-end product strategy and execution for AI-powered systems. Defines requirements, evaluation frameworks, quality standards, and system behavior while balancing quality, latency, cost, reliability, safety, and user experience. Partners closely with ML, backend, and client engineering teams to identify failure modes, improve model performance, prioritize roadmaps, and deliver predictable, useful, trusted AI experiences.