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Learning Analytics Jobs in California (NOW HIRING)

Swish Analytics is a sports analytics, betting and fantasy startup building the next generation of ... The Data Science team is hiring an experienced Machine Learning Engineer with a background building ...

This role sits at the intersection of machine learning, analytics, and operations. You'll work closely with ML engineers, researchers, the Eval Operations Lead, and Memory Developers to ensure ...

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Learning Analytics information

What are the key skills and qualifications needed to thrive in the Learning Analytics position, and why are they important?

To thrive in Learning Analytics, you need strong analytical skills, experience with data analysis, and a background in educational research or instructional design, typically supported by a relevant degree. Familiarity with Learning Management Systems (LMS), statistical tools like R or Python, and certifications in data analytics are commonly expected. Excellent communication skills, problem-solving abilities, and a collaborative mindset help professionals convey insights and work effectively with educators and administrators. These skills are essential for interpreting educational data, driving improvements in teaching and learning, and supporting data-driven decisions in academic environments.

What is a Learning Analytics job?

A Learning Analytics job involves collecting, analyzing, and interpreting data related to learners' performance and educational experiences. Professionals in this field use data-driven insights to improve teaching strategies, personalize learning experiences, and enhance institutional decision-making. They work with various analytical tools, machine learning models, and data visualization techniques to identify patterns and trends. This role is common in educational institutions, corporate training programs, and EdTech companies.

What are typical daily responsibilities for someone working in Learning Analytics?

Professionals in Learning Analytics typically spend their days collecting, cleaning, and analyzing educational data to identify patterns that can improve student outcomes and learning processes. They work closely with faculty, instructional designers, and IT teams to generate reports, visualize trends, and advise on data-backed strategies for curriculum improvement. Day-to-day tasks also involve maintaining data integrity, developing dashboards, and communicating findings in accessible ways to stakeholders. Collaboration and ongoing learning are integral, as the field continually evolves with advances in education technology and analytical methods.

What are the most commonly searched types of Learning Analytics jobs in California? The most popular types of Learning Analytics jobs in California are:
What cities in California are hiring for Learning Analytics jobs? Cities in California with the most Learning Analytics job openings:
Infographic showing various Learning Analytics job openings in California as of June 2026, with employment types broken down into 25% Internship, and 75% Full Time. Highlights an 75% In-person, and 25% Remote job distribution.

Machine Learning Engineer, Reinforcement Learning

Skild AI

San Mateo, CA

Other

Posted 25 days ago


Job description

Position Overview

We are looking for a Machine Learning Engineer to be responsible for designing and implementing cutting-edge reinforcement learning algorithms, conducting experiments, and optimizing these models to perform efficiently in real-world robotic environments. This will require close collaboration with our robotics, research, and engineering team. Your work will directly impact the development of intelligent, adaptable robots capable of learning and performing complex tasks autonomously.

Responsibilities
  • Develop and implement state-of-the-art reinforcement learning algorithms for robotic applications.
  • Design and conduct experiments to train RL models and conduct real-world tests.
  • Collaborate closely with researchers to explore novel methods of scaling up reinforcement learning model training.
  • Communicate effectively with inference, application, and deployment engineers to integrate RL models into robotic systems and iterate on methods to enable robust deployment.
  • Analyze and interpret experimental results, iterating on model design to achieve desired performance.
  • Stay up-to-date with the latest research and advancements in reinforcement learning.
Preferred Qualifications
  • BS, MS or higher degree in Computer Science, Robotics, Engineering or a related field, or equivalent practical experience.
  • Proficiency in Python, C++, or similar and at least one deep learning library such as PyTorch, TensorFlow, JAX, etc.
  • Deep understanding and practical experience with various reinforcement learning algorithms and techniques (model-free, model-based, multi-task, hierarchical, multi-agent, etc.).
  • Strong background in algorithms, data structures, and software engineering principles.
  • Experience with physics simulation engines and tools for training RL.
  • Deep understanding of state-of-the-art machine learning techniques and models.
  • Extensive industry experience with reinforcement learning and robotic systems.