Top Remote Machine Learning Jobs in San Luis Obispo, CA

Manage a team of ML engineers to build and optimize Affirm's economic decisioning engine using advanced ML techniques. Collaborate across teams, provide technical leadership, and mentor engineers while driving a cohesive long-term ML strategy.

The Advisory Agentic Technologist will be the Subject Matter Expert in identity management, driving technical success and strategy, collaborating on agentic AI innovations, and influencing product development, with a focus on the US, Canada, and Latin America.

The Sr. Machine Learning Engineer will develop and deploy ML solutions for healthcare, manage data pipelines, and work with large datasets to enhance healthcare delivery.

As a Staff Applied ML Scientist, you will develop and maintain fraud detection models, conduct research on fraud types, and collaborate across teams to drive product development and real-time decision-making.

As a Senior Applied ML Scientist, you'll develop fraud detection models, research new fraud types, and write production code while collaborating across teams to enhance SentiLink's identity verification solutions.

The Principal AI/ML Engineer will develop machine learning models for campaign optimization in AdTech, collaborating across engineering and product teams to enhance advertising capabilities.

Design, deploy, and maintain end-to-end ML-driven risk solutions at scale to detect and prevent fraud, abuse, and credit risk. Lead technical decisions, build ML tooling and processes, apply state-of-the-art models and third-party data, investigate emerging risk patterns, and collaborate with platform and cross-functional teams to ensure reliable real-time model operation.

Design, deploy, and maintain end-to-end ML risk solutions at scale to detect and prevent fraud, merchant risk, and credit loss. Partner with cross-functional teams, lead technical decisions, build ML tooling, monitor models in production, and investigate emerging abuse patterns to improve detection and decisioning.

The Principal ML Engineer at Agero will develop and productionize a Dispatch System using ML models, optimize decision-making processes, and lead a team to improve operational performance.

As a Senior Machine Learning Engineer, you'll design and develop workflows utilizing vision-language models, manage data pipelines, and collaborate with multiple teams to enhance model accuracy and efficiency.

As a Staff Software Engineer, you will lead the development of computer vision systems for sidewalk inspection, overseeing model lifecycle, system architecture, and mentoring while improving deployment practices.

As a Principal Software Engineer, you'll architect and code backend services and ML models, integrate them with Salesforce, and mentor junior engineers.

In this role, you will lead AI personalization initiatives, develop AI/ML models for user experience, collaborate across teams, and ensure rigorous development practices.

Build and maintain production AI/ML models and pipelines for an NDR platform; develop scalable, secure backend services; optimize detection algorithms and reduce false positives; integrate ML features into product architecture; mentor engineers; participate in code reviews, technical strategy, and documentation; operate cloud-native, containerized services and manage infrastructure as code.

Lead discovery, definition, and delivery of B2B SaaS product features. Own customer research, data-driven prioritization, UX collaboration, KPI tracking, and cross-functional execution with engineering, marketing, and customer success.

Lead enterprise design and deployment of a global operating model for bovine IVF production. Drive standardization, SPC, automation, AI/analytics, Lean/Six Sigma, quality systems, and continuous improvement to scale consistent, high-throughput production across labs and field operations.

Lead pre-sales for Workday Financials-focused AI SaaS: run discovery, demos, technical workshops, respond to RFIs/RFPs, collaborate with Product/Engineering, support sales expansion and market initiatives.

Own a portfolio of large manufacturing accounts, drive net-new pipeline and full-cycle enterprise deals, build C-suite relationships, translate AI/vision capabilities into ROI, execute land-and-expand strategies, and travel 40-50% for on-site engagement and executive reviews.

Lead the commercial engine: build partnership function, close enterprise platform partnerships, own full marketing (positioning, content, paid, website), design AI-leveraged GTM stack, translate product-market fit, run pipeline and commercial reporting, and hire a team when metrics justify growth.

Lead development and integration of predictive pricing models for home insurance, modernize state rating plans, support rate filings, monitor performance and recommend pricing actions, analyze competitor strategies, develop pricing for expansion, build internal pricing analytics software, and coach/mentor analysts.

Manage and grow enterprise airline accounts using Volantio's SaaS platform. Own implementation, launch, optimization, support, renewals, and expansion. Act as trusted advisor, coordinate with Product and Engineering, run regular QBRs and onsite visits, drive retention and revenue expansion, and translate metrics into actionable customer outcomes.

Lead CX transformation engagements by driving client discovery, maturity assessments, journey analysis, and presales solutioning. Design Conversational AI/CCaaS-based solutions, craft proposals and demos, collaborate with sales and delivery, and influence C-level stakeholders to deliver measurable CX improvements and ROI.

Lead and coordinate end-to-end fraud risk engagements, structure analytical solutions, guide analytics teams, communicate with clients and senior stakeholders, manage timelines, build fraud rules/governance, and identify additional client opportunities.

Partner with a sports-sector client to design, build, and deploy end-to-end machine learning solutions. Own modeling lifecycle, feature engineering, validation, automation, and integration with engineering teams. Use SQL and Python to extract data, build models (including NLP for surveys), and deliver reproducible analytics and measurable business impact.

Lead design and deployment of reinforcement learning and sequential decision models for collections and recovery. Build scalable ML pipelines (Databricks/Spark), run experimentation and offline policy evaluation, collaborate with engineering/MLOps to productionize models, and mentor junior data scientists.