Top Machine Learning Jobs in San Luis Obispo, CA

Lead implementations of iQueue for Operating Rooms from discovery through configuration, validation, and rollout. Partner with hospital, clinical, operational, IT, and executive stakeholders to optimize OR workflows, scheduling, staffing, utilization, and access. Responsibilities include data analysis, solution design, product configuration, user acceptance testing, change management, risk monitoring, stakeholder alignment, and go-live readiness. Travel to customer sites is required up to 40–50%.

Own technical wins for strategic enterprise sales, partnering with Account Executives to discover customer needs and develop production-ready solutions. Build proofs of concept, demonstrations, prototype AI agents, and supporting tools while engaging AI teams, data platform teams, and enterprise architects. Apply hands-on experience with LLMs, agents, data science, machine learning, or data engineering to explain how agents retrieve data and use business context.

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

Lead technical client onboarding, migrations, solution design, integrations, data orchestration, troubleshooting, and project delivery for Movable Ink’s AI-powered personalization platform. Partner with clients and internal teams to implement custom workflows, APIs, webhooks, and data solutions. Conduct training and workshops, provide product expertise, advise on roadmap improvements, and support long-term client success across email and mobile marketing programs.

Serve as primary client contact for a portfolio of loyalty-program clients. Scope and manage end-to-end actuarial analyses rooted in predictive modeling and machine learning, validate model outputs, translate results into clear recommendations and presentations, and partner with analysts and data scientists to deliver high-quality, client-facing work in a fully remote consulting environment.

Leads cyber risk assurance strategy and multidisciplinary teams responsible for control monitoring, testing, evidence management, issue remediation, audit support, regulatory readiness, and assurance automation. The role advances continuous, AI-enabled assurance by translating regulatory and cybersecurity requirements into measurable outcomes, maintaining assurance frameworks, evaluating control effectiveness, managing concurrent activities, and reporting metrics to support executive risk decisions.

Lead technical discovery and presales engagements for Zendesk’s AI-powered CX and employee service platform. Design scalable solutions, demos, pilots, and proofs of value; integrate APIs, middleware, telephony, and cloud platforms; and translate AI capabilities into measurable business outcomes. Collaborate with Sales, Product, Engineering, and Customer Success to drive technical strategy, customer adoption, ROI measurement, and product innovation.

Analyze telematics, IoT, sensor, and firmware data to evaluate hardware performance, reliability, and field issues. Build dashboards and reports, identify trends and anomalies, apply statistical and machine learning techniques, validate firmware improvements, and provide actionable insights to engineering, product, data science, and customer support teams. Help develop scalable data pipelines and promote data-driven hardware and software development.

Own delivery of enterprise AI automation projects from planning through rollout. Manage scope, timelines, milestones, budgets, risks, dependencies, client communication, status reporting, and change orders. Coordinate cross-functional teams and stakeholders, lead project meetings, track action items through completion, support user adoption and production handoff, and identify process improvements using AI tools.

Own and negotiate complex commercial transactions, including customer agreements, research collaborations, partnerships, data licenses, SaaS contracts, vendor agreements, and professional services agreements. Partner with sales, science, finance, and R&D teams, particularly on life sciences and computational chemistry deals. Build scalable legal infrastructure through playbooks, templates, fallback positions, self-service processes, and AI-enabled contract workflows.

Leads Lexia’s business analytics and data strategy function, managing analysts and overseeing data engineering alignment. Owns BI strategy, Tableau governance, data governance, self-service dashboards, AI-enabled analytics, metric definitions, and data quality. Partners with Sales, Marketing, Customer Success, executives, and analytics engineering to translate business needs into insights, KPIs, models, and actionable recommendations. Manages analytics tools and vendors while ensuring responsible, secure, and effective use of enterprise data.

Leads technical enablement and sales success for strategic national channel partners. Aligns partner and company go-to-market strategies, develops business plans, conducts technical and sales training, supports evaluations and configurations, and serves as a trusted technical advisor throughout the sales cycle. Builds executive relationships, coordinates training and marketing activities, gathers partner requirements, mentors technical managers, and collaborates with sales, systems engineering, and channel teams.

The Director of Data owns the company’s data platform, governed metrics, client identity resolution, data quality, privacy controls, and AI-ready datasets. This leader manages the data team, establishes governance and operating standards, oversees warehouse and pipeline architecture, and ensures reliable data for Finance, leadership, products, and ML/LLM systems. The role requires hands-on technical leadership, roadmap ownership, stakeholder communication, and accountability for data reliability, lineage, security, and platform costs.

Leads strategy, roadmaps, backlogs, discovery, delivery, and continuous improvement for AI-powered products. Partners with engineering, AI/ML, data, design, and business teams to translate customer needs into measurable solutions using generative AI, LLMs, agents, and automation. Manages stakeholders, product metrics, responsible AI considerations, and complex consulting engagements, including PMO and multi-team delivery responsibilities.

Leads the architecture, design, and implementation of enterprise AI solutions. Defines AI strategies, scalable architectures, technology roadmaps, governance practices, and modern technology stacks. Partners with business and technical stakeholders, guides development teams, evaluates emerging technologies, supports consulting pursuits, and provides technical leadership throughout project lifecycles. The role also mentors architects and engineers and requires client relationship development and travel.

Lead the evolution of trading infrastructure by managing trade operations, P&L attribution, risk monitoring, data integrity, and performance reporting. Develop Python- and machine-learning-based AI agents, MLOps workflows, anomaly detection, predictive risk models, automated reconciliations, and real-time dashboards to transform manual trading operations and improve risk analysis.

Leads clinical intelligence initiatives by sizing AI opportunities, conducting data discovery, evaluating AI/ML solution performance, and measuring impact, accuracy, drift, and patient outcomes. Partners with product, clinical, engineering, compliance, and operations teams to develop measurement plans, value hypotheses, benchmarks, and scalable intelligence capabilities. The role is remote with occasional travel and requires advanced SQL, Python, clinical analytics, and population health research expertise.

Write technically accurate case studies, articles, product explainers, launch materials, and social content for AI engineers, researchers, and technical leaders. Interview customers, partners, and internal experts; learn HUD’s platform and workflows; explain technical products and research clearly; support founder communications; manage content from ideation through publication; and improve content using reader and go-to-market feedback.

Product Counsel will advise product, engineering, data science, marketing, compliance, and sales teams throughout the product lifecycle. Responsibilities include counseling on regulatory compliance, privacy, intellectual property, consumer protection, AI and machine learning, data rights, platform architecture, APIs, identity and account security, and marketing. The role translates legal requirements into technical guidance, supports AI governance, monitors evolving laws, and develops scalable legal processes for a fast-paced technology company.

Leads DISCO’s machine learning research strategy, directs advanced algorithm and methodology development, oversees large-scale research programs, and manages research teams. Collaborates with industry partners and cross-functional stakeholders to apply machine learning to real-world challenges. Develops long-term research plans, mentors researchers, optimizes research processes, and contributes to the broader research community through publications, presentations, and industry engagement.

Develops predictive models, machine learning solutions, data pipelines, statistical analyses, and internal data applications. The role uses Python and SQL Server to analyze structured and unstructured data, applying NLP, classification, regression, clustering, automation, and AI/LLM techniques. Responsibilities include maintaining data science platforms, supporting cross-functional decision-making, and communicating technical findings to non-technical audiences.

Leads financial strategy, planning, forecasting, and operating cadence across Sales, Marketing, Customer Success, and GTM Operations. Owns GTM economics, sales compensation, commissions forecasting, executive reporting, driver-based modeling, resource allocation, and profitability analysis. Modernizes Finance through AI, automation, advanced analytics, improved controls, and scalable processes while developing the GTM Finance team and advising senior executives.

Leads automated scoring and natural language application research for educational assessment products. Responsibilities include setting team priorities, mentoring scientists, designing and analyzing studies, developing machine learning applications, collaborating with stakeholders, presenting technical findings, writing reports, publishing applied research, and supporting products involving automated scoring, crisis-content detection, and formative feedback.

Leads customer-facing technical delivery from demonstrations and requirements discovery through architecture, implementation, testing, and production launch. Builds AI-driven agents, analytical products, predictive modeling workflows, and data pipelines; integrates solutions with client CRMs, consumer applications, and varied data sources. Partners with client technical teams, presents to senior stakeholders, and feeds field learnings back to the core platform team. Requires occasional travel and strong Python, TypeScript, architecture, cloud, and communication skills.

As a Research Engineer, you'll develop and manage data systems for text-to-speech projects and enhance machine learning models using innovative strategies.