Top Remote Machine Learning Jobs in San Diego, CA

The AI Innovation Lead will define and drive AI initiatives, lead projects, foster AI adoption, and collaborate with product teams to enhance AI capabilities at Janea.

Coaches will prepare students for AI and ML roles through resume reviews, interview prep, and guidance on projects, leveraging their industry experience.

As a Senior Machine Learning Engineer, you will architect and build scalable ML infrastructure, influence technical direction, mentor engineers, and ensure effective production deployment, collaborating closely with AI teams.

The VP of Data & AI Engineering leads Duck Creek's enterprise data and AI strategy, overseeing global teams, implementing scalable platforms, and ensuring data governance and responsible AI practices.

The Senior Director, Intelligence leads analytics initiatives, managing teams to develop insights and frameworks that enhance decision-making across Duck Creek Technologies. They ensure scalable and trusted analytics while collaborating with product, engineering, and business stakeholders to drive data-informed strategies and operational excellence.

In this role, you will design and develop AI and machine learning models, maintain data pipelines, evaluate model performance, and collaborate with cross-functional teams to integrate AI capabilities into web-based applications.

The role involves improving machine learning ranking models, implementing algorithms, and taking full ownership of machine learning systems within Quora's product.

As a Machine Learning Engineer, you'll design and implement detection systems, enhance model efficacy, and analyze datasets to improve email detection against security threats.

The role involves developing, adapting, and implementing machine learning algorithms for computer vision tasks in power grid analysis. Responsibilities include experimentation, error analysis, and project ownership.

As a Senior ML Engineer, you will lead the design and operation of GenAI solutions, focusing on LLM development and deployment, mentoring colleagues, and collaborating with cross-functional teams to enhance AI capabilities for the insurance sector.

As a Staff Machine Learning Engineer at Twilio, you will build and maintain data pipelines and ML infrastructure, ensuring high-quality data and model performance while collaborating with product and security teams.

The Director of Engineering will lead and scale Buzz Solutions' engineering organization, focusing on AI, infrastructure, and platform systems while ensuring best practices and high-quality solutions.

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 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.

Serve as an AI/ML-focused solution engineer for Healthcare & Life Sciences: provide technical guidance, design cloud data and lakehouse architectures on Snowflake, lead demos and proofs-of-concept, support sales cycles, collaborate with product and engineering, and communicate AI/ML value to technical and executive stakeholders.

The Sales Executive will develop new AI and ML service opportunities, manage sales pipelines, and engage with technical decision-makers primarily in the US and Canada. Responsibilities include proposal shaping, pipeline management, and client relationship management with a focus on complex technology sales.

The Machine Learning Engineer will design, develop, and deploy ML models using Google Cloud tools, focusing on Document AI and Vertex AI, ensuring scalable applications and efficient performance.

Design and implement infrastructure to support generative AI and machine learning, optimizing workflows and integrating new technologies while ensuring reliability and performance.

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.

As an ML Platform Engineer, you will enhance our core ML platform's performance and scalability, collaborating with teams to innovate solutions.

Build and design backend services, AI agents, and machine learning models. Collaborate cross-functionally to deliver AI features and maintain code quality.

The Senior ML Data Scientist will develop machine learning models to detect fraud, manage the data science lifecycle, and collaborate with teams on strategies.

The Principal Machine Learning Engineer will define technical direction, architect scalable systems, lead cross-organizational initiatives, mentor engineers, and communicate effectively at all levels.

Build and improve machine learning models and systems to classify and enrich Internet data, collaborating with engineering, research, and product teams.