Top Remote Machine Learning Jobs in Santa Cruz, CA

Analyze and optimize digital partnership performance, growth, profitability, and customer acquisition economics. Design experiments, develop forecasts and performance models, build measurement frameworks, investigate anomalies, and provide actionable recommendations. The role requires translating ambiguous business questions into analytical solutions, communicating insights to technical and non-technical stakeholders, and establishing scalable analytical processes that support strategic decision-making.

Own the end-to-end product direction and quality of an AI email application. Define AI and system requirements, translate model capabilities into product decisions, balance quality, cost, latency, reliability, safety, and user experience, and partner with ML, backend, and client engineers. Establish evaluation frameworks, quality standards, feedback loops, and failure recovery strategies while driving prioritization, execution, experimentation, and user trust in a zero-to-one environment.

Own the end-to-end product direction and quality of an AI-powered email application. Define AI and system requirements, evaluation frameworks, quality standards, and measurable outcomes. Partner closely with ML, backend, and client engineering teams to make trade-offs across quality, latency, cost, reliability, safety, and user experience. Drive prioritization, specifications, experimentation, failure-mode mitigation, and continuous iteration in a technical zero-to-one environment.

Own the end-to-end product direction and quality of an AI-powered email application. Define AI and system requirements, translate model capabilities into product decisions, establish evaluation frameworks, and balance quality, latency, cost, safety, reliability, and user experience. Partner closely with ML, backend, and client engineers to identify failure modes, improve system behavior, prioritize execution, and build predictable, trustworthy AI experiences.

Own the end-to-end lead management lifecycle, including capture, enrichment, qualification, scoring, routing, sales handoff, disposition, and recycling. Establish governance, business rules, SLAs, controls, and performance metrics; optimize workflows and integrations; resolve data quality and lead-flow issues; and partner with Sales, Marketing, Data Science, IT, and Analytics. Manage and develop a small team while overseeing operational priorities, testing, documentation, and continuous improvement.

Lead application engineering for an AI-powered email product across backend, mobile, and desktop platforms. Design reliable AI workflows involving orchestration, state, tool use, retries, fallbacks, and failure handling. Build APIs connecting AI capabilities to product experiences, make architectural trade-offs, establish observability and operational excellence, and lead a small team of senior engineers through hands-on technical mentorship and code development.

Own the end-to-end strategy, requirements, quality, and execution of an AI-powered email product. Partner closely with ML, backend, and client engineers to define system behavior, evaluate model performance, manage trade-offs across quality, latency, cost, reliability, safety, and UX, and establish feedback loops. Drive prioritization, specifications, experimentation, failure handling, and product quality in a technical zero-to-one environment.

Lead hands-on engineering for an AI-powered email application across backend, mobile, and desktop platforms. Design reliable AI workflows involving orchestration, state, tool use, retries, fallbacks, and failure handling. Define APIs and abstractions, make architecture and scalability decisions, improve observability and reliability, and lead a small team of senior engineers through technical reviews, mentorship, and production delivery.

Lead the architecture and development of reliable AI-powered email applications across backend, mobile, and desktop platforms. Design APIs and workflow systems supporting orchestration, state, tool use, retries, fallbacks, and failure handling. Lead a small senior engineering team, make architectural trade-offs, mentor engineers, and establish strong production practices including observability, monitoring, testing, scalability, security, and operational excellence. Partner with ML leadership to deliver robust, high-quality product experiences.

Lead the engineering strategy for an AI-powered email application across backend, mobile, and desktop platforms. Design reliable AI workflows with orchestration, state management, tool use, retries, fallbacks, and failure handling. Build APIs connecting AI capabilities to product experiences, lead a senior engineering team, make architectural trade-offs, and establish strong observability, monitoring, testing, reliability, and scalability foundations while partnering with ML leadership.

Lead the technical side of enterprise sales for autonomous systems and physical AI customers. Own discovery, architecture reviews, demonstrations, proofs of concept, evaluations, deployments, and expansions. Translate customer workflows into solutions, address technical objections, and collaborate with Product and Engineering. Build scalable sales engineering processes, technical content, evaluation frameworks, and operating models while supporting strategic automotive OEM and enterprise opportunities.

Lead product strategy, requirements, and roadmap development for Honeywell Forge cloud applications serving utilities. Own AI and analytics capabilities for grid operations, DER management, rate design, and utility programs. Conduct customer and market discovery, competitive analysis, and voice-of-customer research. Partner with solution architects, sales, customers, and engineering to prioritize and deliver B2B SaaS products that generate measurable utility operational value.

Lead product strategy, requirements, roadmap, and AI capabilities for Honeywell Forge cloud applications serving utilities. Conduct customer and competitive discovery, translate utility workflows into product investments, define commercial opportunities, and partner with engineering to prioritize and deliver cloud-native B2B software. The role focuses on AI for grid operations, DER management, rate design, and utility program performance while ensuring measurable operational value.

Own the global consumer engagement and personalization domain across digital channels. Assess current capabilities, define multi-year strategy and roadmap, recommend personalization infrastructure investments, and translate priorities into shipped products. Partner with engineering, data science, design, marketing, legal, and regional teams to deliver scalable experiences. Establish metrics, run cross-market experiments, optimize performance, and make global versus localized decisions. Influence executives, mentor product managers, and apply AI, identity resolution, and generative AI to personalization.

Deploys and optimizes enterprise infrastructure across on-premises systems, networks, AWS, Azure, and Microsoft 365. Engineers Terraform automation, Intune endpoint management, AI initiatives, SaaS integrations, SSO, and IT service workflows. Designs and maintains cybersecurity architecture, Zero Trust controls, security baselines, monitoring, and compliance controls aligned with CIS, NIST, and SOC 2. Provides advanced troubleshooting, technical support, VoIP administration, vendor coordination, and root-cause remediation.

Manage the AI opportunity roadmap for Risk Services, prioritizing use cases, coordinating internal data science teams and external vendors, translating business needs into requirements, and supporting AI governance, compliance, explainability, auditability, implementation, adoption, and benefits realization. The role focuses on business outcomes and solution delivery rather than hands-on model development.

Designs, develops, deploys, maintains, and optimizes ETL processes and cloud-based system integrations for healthcare data warehousing and business intelligence. Works with DHA and Cerner inpatient and outpatient data, develops database procedures, validates migrations, performs data mapping and modeling, and supports data science and machine learning teams. The role also documents processes, tunes performance, captures client requirements, and manages data transformation initiatives.

Own and develop SandboxAQ’s intellectual property function, including patent portfolio strategy, invention harvesting, prosecution, lifecycle management, outside counsel oversight, freedom-to-operate analyses, trademark clearance, and trade secret programs. Partner with scientists, engineers, and business teams to protect innovations, inform product strategy, support transactions, and build scalable IP processes in a fast-growing AI-focused company.

Manage a team of data scientists to develop fraud detection models, lead planning and collaboration with leadership, and communicate outcomes effectively while maintaining strong business intuition and technical leadership.

Design, build, and operate production ML decision systems to detect and prevent payment fraud, account takeover, identity abuse, merchant/marketplace risk, scams, and other adversarial activity. Own end-to-end production lifecycle: data contracts, low-latency inference, batch scoring, feature quality, deployment, monitoring, incident response, rollback, and feedback loops. Develop AI-assisted workflows and reusable decision capabilities while partnering with modelers, product, compliance, and operations.

Design, build, and operate production ML signal systems—ranking, retrieval, recommendations, propensity, and next-best-action—covering feature/candidate generation, serving, experimentation, monitoring, and feedback. Define signal contracts (freshness, provenance, confidence), evaluate long-term impact (trust, fairness, compliance), and partner across product, data, and risk teams to deliver reusable customer-intelligence capabilities.

Own the research-to-production pipeline: harden training/evaluation workflows, build packaging/deployment tooling, design release gates, optimize inference and serving for latency/throughput, establish benchmarking and validation, and instrument feedback from production to accelerate research iteration.

Design, develop, and deploy machine learning models and pipelines to optimize operations. Lead full ML project lifecycle, ensure model evaluation/monitoring, collaborate cross-functionally, mentor junior engineers, and drive continuous improvement in ML applications and processes.

Advise clients on antitrust eDiscovery compliance using machine learning and generative AI. Build and validate TAR/AI models (Epiq AI, Relativity, Reveal, Brainspace), craft LLM prompts, draft defensible protocols, train/transfer workflows to review teams, and support client-facing, pre-sales, and thought-leadership activities. Must manage multiple projects and meet deadline-driven schedules.

Leads and scales the post-sale customer success organization, overseeing customer retention, expansion, adoption, renewals, and customer outcomes. Manages 20 or more Customer Success Managers and Leads, develops scalable lifecycle processes, integrates AI into customer success operations, and partners cross-functionally on product and revenue strategy. The role also manages strategic accounts, international expansion across Mexico and Latin America, team development, performance metrics, customer advocacy, and operational excellence.