1

Patterned Learning Ai Jobs in California (NOW HIRING)

Applied AI Engineer

San Francisco, CA · On-site +1

$150K - $300K/yr

Experiment with the latest LLM orchestration patterns and context management techniques for ... learning, AI research, or applied AI engineering * Strong background in LLMs, RAG systems, and ...

Set and uphold coding standards, architectural patterns, and review processes; mentor engineers. Co ... Passion for engineering and learning. * AI fluency, and ability to build with AI at the core

Intern

San Francisco, CA · On-site

$17.75 - $23.50/hr

Discover patterns, trends, and predictive signals in large datasets using statistical modeling, machine learning, and AI techniques. * Apply data segmentation, forecasting, and optimization methods ...

AI's strategic objectives. Key Responsibilities: * Design, build, and maintain end-to-end ML ... Solid foundation in computer science fundamentals (data structures, algorithms, design patterns ...

AI's strategic objectives. Key Responsibilities: * Design, build, and maintain end-to-end ML ... Solid foundation in computer science fundamentals (data structures, algorithms, design patterns ...

ServiceNow Solution Architect - US

San Jose, CA · On-site

$73.75 - $97.25/hr

... patterns. Learning Platform Enablement • Partner with engineering teams to translate business requirements into technical designs. • Define technical architecture for AI-powered Learning ...

ServiceNow Solution Architect - US

San Jose, CA

$73.75 - $97.25/hr

... patterns. Learning Platform Enablement Partner with engineering teams to translate business requirements into technical designs. Define technical architecture for AI-powered Learning including in ...

About Bretton AI Bretton AI is the leading AI agent platform for financial services. Companies like ... Ability to review complex information and identify patterns or issues * Comfortable learning new ...

next page

Showing results 1-20

Patterned Learning Ai information

What are some typical challenges faced by Patterned Learning AI professionals in implementing AI-driven solutions within organizations?

Patterned Learning AI professionals often encounter challenges such as integrating AI models with existing legacy systems, ensuring high-quality and representative training data, and aligning AI solutions with specific business objectives. Collaboration across multidisciplinary teams—including data scientists, software engineers, and business stakeholders—is essential for successful deployment. Additionally, professionals must stay updated on evolving AI technologies and best practices to maintain model accuracy and address ethical considerations.

What are the key skills and qualifications needed to thrive as a Machine Learning Engineer, and why are they important?

To thrive as a Machine Learning Engineer, you need a strong background in mathematics, statistics, programming (especially Python), and a degree in computer science or a related field. Experience with machine learning frameworks such as TensorFlow, PyTorch, and scikit-learn, as well as familiarity with cloud computing platforms and data management tools, is essential. Excellent problem-solving skills, creativity, and clear communication are crucial soft skills for collaborating with teams and translating complex models into practical solutions. These competencies are vital for developing reliable AI systems that solve real-world problems and drive innovation.

What is the difference between Patterned Learning Ai vs Data Scientist?

AspectPatterned Learning AiData Scientist
Required CredentialsTypically requires machine learning, AI, or computer science degrees; certifications in AI toolsRequires degrees in statistics, computer science, or related fields; often certifications in data analysis
Work EnvironmentTech companies, AI startups, research labs focusing on AI developmentBusiness, finance, healthcare, and tech sectors analyzing data for insights
Employer & Industry UsageUsed by AI-focused organizations developing intelligent systemsEmployed across industries for data analysis, predictive modeling, and decision support

Patterned Learning Ai primarily focuses on developing AI models and algorithms, often requiring specialized technical skills. Data Scientists analyze data to extract insights and inform business decisions. While both roles involve data and machine learning, Patterned Learning Ai is more centered on creating AI systems, whereas Data Scientists interpret data for strategic purposes.

What is Patterned Learning AI?

Patterned Learning AI refers to artificial intelligence systems designed to recognize, learn from, and replicate patterns in data. These systems use algorithms to identify trends, correlations, and structures within large datasets, enabling them to make predictions or automate decision-making processes. Patterned Learning AI is commonly used in fields like image recognition, natural language processing, and predictive analytics. Its applications help businesses and researchers uncover hidden insights, streamline operations, and improve accuracy in various tasks.
What cities in California are hiring for Patterned Learning Ai jobs? Cities in California with the most Patterned Learning Ai job openings:
Infographic showing various Patterned Learning Ai job openings in California as of June 2026, with employment types broken down into 53% Full Time, 45% Part Time, and 2% Contract. Highlights an 66% Physical, 4% Hybrid, and 30% Remote job distribution.
Senior/Staff Machine Learning Engineer, General Agents, Enterprise GenAI

Senior/Staff Machine Learning Engineer, General Agents, Enterprise GenAI

Scale AI

San Francisco, CA • On-site

Full-time

Posted yesterday


Job description

Job Summary:
Scale AI is the data foundation for AI, helping organizations build and deploy reliable production AI applications. As a Senior/Staff Machine Learning Engineer on the General Agents team, you’ll design, build, and deploy production-ready AI agents that address high-impact enterprise problems, working across the full agent lifecycle.
Responsibilities:
• Design and implement end-to-end agent systems that combine LLM reasoning, tool use, memory, and control logic to solve recurring enterprise use cases.
• Build scalable, reliable agent architectures that can be deployed across many customers with varying data, tools, and constraints.
• Develop evaluation frameworks, datasets, environments, and metrics to measure agent performance, reliability, and business impact in production settings.
• Collaborate closely with product managers, customers, data annotators, and other engineering teams to translate enterprise requirements into robust agent designs.
• Productionize frontier agent techniques (e.g., planning, multi-step reasoning and tool-use, multi-agent patterns) into maintainable, observable systems.
• Own deployment, monitoring, and iteration of agent systems, including failure analysis and continuous improvement based on real-world usage.
• Contribute to technical direction and architectural decisions for general agent development best practices and methods, with increasing scope and leadership at the Staff level.
Qualifications:
Required:
• 5+ years of experience building and deploying machine learning or AI systems for real-world, production use cases.
• Strong engineering fundamentals, supported by a Bachelor’s and/or Master’s degree in Computer Science, Machine Learning, AI, or equivalent practical experience.
• Deep understanding of modern LLMs, prompt-, context-, and system-level optimization, and agentic system design.
• Proven proficiency in Python, including writing production-quality, testable, and maintainable code.
• Experience building systems that integrate models with external tools, APIs, databases, and services.
• Ability to operate in ambiguous problem spaces, balancing research-driven approaches with pragmatic product constraints.
• Strong communication skills and comfort working in customer-facing or cross-functional environments.
Preferred:
• Hands-on experience building AI agents using modern generative AI stacks (OpenAI APIs, commercial or open-source LLMs).
• Experience with agent frameworks, orchestration layers, or workflow systems (e.g., tool calling, planners, multi-agent setups).
• Familiarity with evaluation, monitoring, and observability for LLM-powered systems in production.
• Experience deploying ML systems in cloud environments and operating them at scale.
• Experience fine-tuning or adapting foundation models using methods like supervised fine-tuning (SFT), reinforcement learning with verifiable rewards (RLVR), and low-rank adaptation (LoRA) to improve agent performance on domain-specific tasks.
• Interest in shaping the future of general-purpose enterprise agents and their real-world impact.
Company:
Scale’s mission is to develop reliable AI systems for the world’s most important decisions. Founded in 2016, the company is headquartered in San Francisco, USA, with a team of 501-1000 employees. The company is currently Late Stage.