Top Remote Machine Learning Jobs in San Luis Obispo, CA

Lead the design and delivery of a production-grade localization ML stack for autonomous vehicles: architect models for landmark-based localization across sensors, own data and evaluation pipelines, drive feature development to deployment, define failure modes and safety coverage, and mentor engineers while integrating cutting-edge multimodal and foundation-model techniques into robust, real-time localization systems.

Design and deliver enterprise-grade Generative AI solutions on AWS using Bedrock and AgentCore. Architect LLM-based applications, RAG pipelines, agentic workflows, vector DB integrations, and APIs. Optimize models for performance, cost, and quality, ensure security and governance, lead teams, and troubleshoot production GenAI systems.

Architect and implement enterprise-grade MLOps/LLMOps platforms on AWS and Kubernetes. Design ML/LLM pipelines for training, validation, deployment, monitoring, governance, CI/CD, and observability. Serve as technical authority, conduct architecture reviews, ensure security and compliance, and mentor engineering teams across projects.

Design, build, and operate production agentic AI systems on AWS Bedrock AgentCore using Strands/LangGraph. Implement tool integrations (MCP/Gateway), evaluation pipelines and LLM-as-judge CI gates, observability, cost/latency controls, and guardrails for agents operating on live dealer data.

Design, implement, and own a reusable on-device ML inference platform for resource-constrained devices. Integrate inference into RTOS firmware, develop drivers and DMA paths, debug on real hardware, train and optimize models for microcontroller-class hardware, and ship features to production while raising team expertise and using AI-assisted tooling.

Build, train, fine-tune, evaluate, and deploy machine learning and LLM models for production. Create monitoring, evaluation, and feedback tooling, evaluate new techniques, and collaborate cross-functionally to ship ML-powered features.

Design, develop, implement, and monitor statistical and machine learning models to support credit risk, collections, servicing, marketing, and forecasting. Build data pipelines, translate business needs into analytical solutions, support model governance, testing, deployment, and ongoing performance monitoring, and communicate results to stakeholders.

Lead development and deployment of predictive and ML models for credit risk, collections, and operations. Mentor data scientists, promote MLOps and modern tools, present insights to stakeholders, and drive experimentation with techniques such as causal inference and GenAI.

Partner with customers to design and deliver production-ready AI/ML solutions, drive POC-to-production success, troubleshoot technical issues, inform product roadmap with customer feedback, and communicate outcomes to stakeholders.

The Senior Data Engineer will own the data strategy, managing sourcing, structuring, and optimizing multimodal data to enhance AI models and products.

As a Senior Software Engineer, you'll build and operate the ML platform, optimizing data operations, managing the feature store, and supporting ML initiatives while driving architectural roadmap decisions.

The Staff Engineer will architect and build AI and ML infrastructures, mentor engineers, and lead high-impact initiatives to enhance ML capabilities across Attentive's platform.

As a Senior Machine Learning Engineer, you will develop and deploy AI-driven products to enhance clinical trials, collaborating with teams to improve patient care through scalable machine learning solutions.

Own and evolve ClickHouse's Python connector and SDKs; build and maintain integrations with orchestration and transformation tools; design AI/LLM integration patterns (RAG, embeddings, feature stores); engage with open-source community; collaborate with Product and Cloud teams to prioritize and ship production-grade data integrations.

Develop AI/ML-powered features in ClickHouse Cloud, focusing on frontend and API architecture, UI/UX, and integrations with AI systems.

The Senior Principal AI/ML Architect will drive AI architectural vision, influence multi-squad strategy, design intelligent systems, and set operational standards for Yahoo Mail's AI capabilities.

Lead development of cutting-edge LLM and Agentic AI systems for Airbnb customer support, from research and prototyping to production deployment. Collaborate with product, design, and engineering to build scalable ML infrastructure, improve model performance, and implement evaluation, fine-tuning, and guardrails for chat and voice assistants.

Develop and tune ML models, productionize and deploy strategies for ML systems, and align cross-functionally to communicate performance metrics.

As a Staff Machine Learning Engineer, you will develop, productionize, and deploy ML models for clinical use, ensuring real-world effectiveness through hands-on coding and infrastructure building.

As an ML Engineer at Luma AI, you will design high-performance model serving pipelines, manage GPU resources, and optimize CI/CD for large-scale AI systems.

The Staff Machine Learning Engineer will develop and maintain MLOps tools, optimize model performance, and collaborate on ML system architecture with a focus on enhancing developer experience.

Develop, validate, and improve machine learning models (regression, classification, forecasting, optimization). Prepare data, perform feature engineering, train and evaluate models, and support deployment with Data/ML Engineering. Communicate results, document methods, and collaborate on experiments and cross-functional initiatives.

Build and maintain Quora's ML platform and ranking infrastructure to serve large-scale ML models. Work on GPU model serving, distributed systems, developer tooling, feature store modernization, and on-call rotation to ensure high availability, performance, and cost efficiency.

The ML Engineer will leverage deep learning for natural language processing tasks, impacting Entefy's products while requiring strong machine learning fundamentals and NLP expertise.

Lead end-to-end development of fraud and risk ML systems: define problems, engineer features, build and evaluate models, collaborate with product and engineering, deploy with ML engineers, and monitor production performance to detect drift and trigger retraining.