Top Machine Learning Jobs in San Luis Obispo, CA

Lead the strategy, design, deployment, and scale-up of AI-powered health products across the Global South. Own product roadmaps, execution, government-system integration, stakeholder adoption, partnerships, grant development, and portfolio expansion. Collaborate with engineering, machine learning, program, and domain teams to deliver sustainable, measurable public-health impact.

Support analytics by cleaning, validating, and transforming data; build dashboards and KPI reports; perform trend, cohort, and A/B testing analyses; improve data pipelines and forecasting; present insights to cross-functional teams.

Build and deploy agentic and generative AI systems that automate risk and compliance investigations. Own full-stack ML systems, including data integrations, prompt tuning, orchestration, evaluation, productionization, monitoring, and transformer-based modeling across transaction, graph, text, and profile data. Collaborate with product, engineering, platform, and operations teams while leading strategic roadmaps and delivering scalable production systems.

Participants are needed for an AI technology research study, offering compensation up to $600 for ~1.5-2 hours of participation over one week, requiring skills in tech-related roles.

Build and deploy agentic and generative AI copilot and autopilot systems for risk and compliance automation. Own end-to-end ML systems, including data integrations, prompt tuning, orchestration, evaluation, productionization, monitoring, and transformer-based model development. Collaborate with product, engineering, platform, and operations teams to improve fraud detection and analyst productivity. Drive strategic roadmaps and cross-functional programs with significant business impact.

Lead post-sales success for enterprise SaaS customers, drive adoption, monitor account health and model performance, manage escalations, run QBRs, partner cross-functionally to ensure renewals and expansions, and deliver measurable business outcomes while advising on identity verification and fraud detection solutions.

Performs advanced physical and remote insurance premium audits for business clients. Reviews business records, interviews owners and managers, investigates exposures, documents findings, prepares written audit reports, schedules assignments, communicates results, and submits completed work for quality review. The auditor must manage deadlines independently, correct returned audits, maintain accurate records, and use Microsoft Office applications. This remote role requires reliable transportation, a valid driver’s license, strong analytical and communication skills, and at least five years of premium insurance auditing experience.

Leads enterprise omnichannel strategy and transformation for pharmaceutical and healthcare clients. Builds customer engagement models across media, CRM, field, digital, and support channels; develops next-best-engagement capabilities using AI, analytics, and customer data; owns omnichannel technology roadmaps, journey design, governance, measurement, and change management. The VP leads a Center of Excellence, mentors senior leaders, manages budgets, supports business development, and coordinates strategy, media, creative, medical, analytics, technology, and commercial teams.

Leads the company’s adoption of AI, analytics, automation, and business intelligence. Builds AI-powered workflows, Excel solutions, natural-language data interfaces, dashboards, forecasts, predictive analytics, and automated reporting using Snowflake, SQL, Tableau, and related tools. Partners across business functions to identify opportunities, improve data quality, automate repetitive processes, and deliver scalable decision-support solutions. Establishes responsible AI practices and advances an AI-first analytics culture.

Clean, standardize, enrich, and import product data into the PIM while maintaining data integrity and governance standards. Oversee bulk ingestion processes, leverage AI and automation, support data model improvements, resolve product listing issues, and contribute to data hygiene and workflow modernization. The role also involves collaborating with client-facing teams, documenting processes, proofreading product data, communicating progress, and adapting to changing priorities.

Lead data engineering and machine learning initiatives by designing, architecting, developing, and supporting GCP data pipelines. Create information architecture documentation, estimate and plan technical work, collaborate with project teams and business analysts, and translate requirements into technical designs. Develop Python-based ELT pipelines, optimize BigQuery SQL, support CI/CD, and provide technical leadership to projects and teams.

Own discovery, definition, and delivery of marketplace directory features for Avetta’s B2B SaaS platform. Serve as the customer advocate, conduct research and usability testing, analyze product metrics, prioritize problems, define KPIs, and collaborate with engineering, UX, marketing, and customer success. The role focuses on scalable marketplace and platform products involving search, profiles, matching, recommendations, onboarding, monetization, trusted data, and network effects.

Reviews and refines AI-generated content and data outputs, develops prompts, evaluates model performance using rubrics, conducts independent research and fact-checking, annotates data, and summarizes complex findings in technical reports. Works asynchronously with project leads and domain experts to improve AI output quality and analytical standards.

Lead the vision, strategy, roadmap, and global execution of AI-powered learning and workforce skilling products. Manage and mentor product managers, collaborate with engineering, design, research, marketing, and customer teams, and drive products from customer insight through launch and scale. Build AI-native experiences, improve product adoption and metrics, foster inclusive team practices, and deliver measurable outcomes for millions of learners.

Own and modernize Apartment List’s B2C lifecycle marketing program across email, push, and SMS. Build segmentation, personalization, partnerships, cross-sell, forecasting, experimentation, and measurement systems. Lead analytics, attribution, deliverability, and revenue optimization while remaining hands-on with journeys, templates, dynamic content, audience logic, SQL, and troubleshooting. Manage a two-person team, partner cross-functionally on roadmap priorities, and oversee the Iterable relationship.

Own strategic enterprise accounts across expansion and new-logo sales motions. Build executive relationships, develop account plans, run account-based outbound, navigate complex buying committees, deliver consultative demonstrations, negotiate contracts, and close high-value SaaS deals. Partner with Customer Success, Business Development, and Marketing while maintaining Salesforce pipeline and forecasts. Success requires consistently meeting quota through longer-cycle, multi-stakeholder deals and contributing to the evolving enterprise sales playbook.

Drive new-account acquisition and cloud migrations for AI and infrastructure solutions. Generate pipeline through global outbound prospecting, execute multichannel outreach, manage the full enterprise sales cycle, and close large cloud, infrastructure, and data center deals. The role requires deep knowledge of AI computing, cloud infrastructure, power capacity, cooling systems, financial modeling, and B2B enterprise net-new cloud sales. Candidates work from Akamai’s San Francisco office and collaborate across time zones.

Own technical product marketing for Bloomreach’s APIs, integrations, and agentic AI platform. Translate platform capabilities into positioning, use cases, partner launch strategies, customer storylines, sales enablement, and go-to-market materials. Collaborate with Product, Engineering, Solutions Engineering, partners, sales, and technical buyers. Define launch metrics, coordinate milestones, communicate product value, and evaluate post-launch results.

Design and deploy machine learning models for financial document processing, optimize large language models, collaborate with cross-functional teams, and guide junior peers in a fast-paced environment.

The Senior Staff Machine Learning Scientist will lead research initiatives in computer vision and multimodal understanding, develop novel models, and translate research into practical applications while mentoring team members.

Leads a team of machine learning engineers and data scientists developing and deploying styling algorithms and AI-powered personalization experiences. Defines AI and ML interventions, shapes product roadmaps, oversees experimentation and production deployment, and partners with product, design, merchandising, styling, and business teams. Establishes best practices for collaboration, code quality, AI use, and data management while mentoring team members and communicating technical concepts to stakeholders.

Design and prototype sensors (acoustic, ultrasonic, etc.), develop signal-processing algorithms, automate data acquisition and analysis, evaluate performance in lab/field, define requirements and architecture, produce documentation, integrate with mechanical/electrical/software teams, troubleshoot hardware, and support IP activities.

Leads Stitch Fix’s Product Algorithms portfolio across Growth, Styling, and Fix & Freestyle. Defines strategy and roadmaps for machine learning, AI, experimentation, personalization, and product analytics; drives scalable production capabilities and measurable business outcomes; improves planning, operational readiness, incident management, and decision-making; and builds a high-performing organization of managers and senior technical contributors while partnering with executive and cross-functional stakeholders.

Own the strategy, requirements, evaluation, and execution of AI agent products and systems. Partner closely with ML, backend, and mobile engineers to make trade-offs across quality, latency, cost, reliability, and user experience. Define evaluation frameworks, drive prioritization, and ensure AI features are understandable, predictable, safe, and trusted. Translate model capabilities and feedback into product decisions and reliable real-world workflows.

Own the strategy, requirements, evaluation, and reliability of AI agent products. Partner closely with ML, backend, and mobile engineers to make trade-offs across quality, latency, cost, reliability, and user experience. Define offline and online evaluation frameworks, drive product execution, and ensure AI features are correct, predictable, safe, and trusted. Translate model capabilities and feedback into product decisions, roadmaps, and rapid iteration.