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Gradient Learning Jobs in California (NOW HIRING)

Spotter

Machine Learning Scientist

Culver City, CA · On-site

Solid grasp of core RL training objectives and loss functions, including temporal-difference and Bellman error losses (Q-learning, DQN), policy gradient objectives (REINFORCE, actor-critic advantage ...

Spotter

Machine Learning Scientist

Culver City, CA · On-site

Solid grasp of core RL training objectives and loss functions, including temporal-difference and Bellman error losses (Q-learning, DQN), policy gradient objectives (REINFORCE, actor-critic advantage ...

Atoms

Senior Machine Learning Engineer

Mountain View, CA · On-site

$175K - $230K/yr

LLMs, GNN, Deep Learning, Logistic Regression, Gradient Boosting trees, etc. * Working knowledge in one or more of the following: generative AI, data mining, information retrieval, advanced ...

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How much do gradient learning jobs pay per hour?

As of Aug 20, 2026, the average hourly pay for gradient learning in California is $41.54, according to ZipRecruiter salary data. Most workers in this role earn between $21.42 and $57.44 per hour, depending on experience, location, and employer.

What is Gradient Learning?

Gradient Learning is an education-focused nonprofit organization that partners with schools and educators to develop innovative teaching tools and learning models. Their mission is to support student-centered education by providing resources such as curriculum content, professional development, and technology platforms. Gradient Learning is known for initiatives like the Summit Learning program, which emphasizes personalized learning, project-based instruction, and strong teacher-student relationships. They collaborate with schools to improve educational outcomes and empower teachers to tailor learning experiences to individual student needs.

How does a Gradient Learning professional collaborate with educators and technology teams to implement personalized learning solutions?

Professionals working in Gradient Learning roles often serve as a bridge between educators and technology teams, ensuring that personalized learning platforms are effectively integrated into classroom environments. They collaborate with teachers to understand classroom needs, provide training, and gather feedback to refine digital tools. Simultaneously, they work closely with developers and product managers to relay user insights and help prioritize features that enhance student learning experiences. This cross-functional collaboration is essential for creating solutions that are both pedagogically sound and technically robust.

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

To thrive as a Gradient Learning specialist, you need expertise in instructional design, educational technology integration, and a background in teaching or curriculum development, often supported by a relevant degree. Familiarity with learning management systems (LMS), digital assessment tools, and platforms like Google Classroom is common in this role. Strong communication, collaboration, and problem-solving skills are essential for engaging educators and supporting student-centered learning. These skills ensure the effective implementation of personalized learning strategies and foster successful educational outcomes.

What are popular job titles related to Gradient Learning jobs in California?

For Gradient Learning jobs in California, the most frequently searched job titles are:

What job categories do people searching Gradient Learning jobs in California look for?

The top searched job categories for Gradient Learning jobs in California are:

Gradient Learning jobs near you
Infographic showing various Gradient Learning job openings in California as of August 2026, with employment types broken down into 1% As Needed, 80% Full Time, 18% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $86,395 per year, or $41.5 per hour.

Machine Learning Engineer (Infra), Driver Understanding and Evaluation

Waymo

Mountain View, CA • On-site

Full-time

Re-posted 4 days ago

Job description

Job Summary:
Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. The role involves building scalable machine learning systems for evaluating driving behaviors and optimizing machine learning models for autonomous vehicles.
Responsibilities:
• Build scalable systems for training and fine-tuning large-scale models to evaluate interesting driving behaviors.
• Work at the intersection of data engineering, model development, and simulation Provide guidance on architectural decisions and technical directions.
• Own large, complex systems, driving architectures that meet technical and business objectives.
• Contribute to the production and optimization of machine learning models aiming to assess Waymo’s expansive fleet of vehicles that cumulatively travel millions of miles.
• Design and scale large distributed systems covering the ML lifecycle, supporting planet-scale dataset generation, model training, and evaluation.
• Collaborate cross-functionally to derive performance and system-level requirements for large ML systems.
• Translate product/business goals into measurable technical deliverables, ensuring system component alignment.
Qualifications:
Required:
• M.S. or Ph.D. degree Computer Science, Machine Learning, Artificial Intelligence, or a related technical field, or equivalent practical experience.
• 3+ years in machine learning infrastructure such as developing, designing, scaling, training, deploying, and optimizing large-scale machine learning systems from data to model.
• A history of contributions to machine learning tooling and frameworks e.g. PyTorch, Jax, Tensorflow, Ray, or similar. The candidate should understand both the user facing API and the internal workings.
• Strong expertise in distributed training techniques, including gradient sharding and optimization strategies for scaling large models across ML accelerator profiling tools to uncover performance bottlenecks.
Preferred:
• 5+ years in machine learning infrastructure such as developing, designing, scaling, training, deploying, and optimizing large-scale machine learning systems from data to model.
• Experience in the autonomous vehicles domain, robotics, or complex simulation environments.
• Familiarity with large-scale simulation platforms and their integration with ML training workflows.
Company:
Waymo is a mobility technology company that improves transportation by developing self-driving solutions for travelers and daily commuters. It is a sub-organization of Alphabet. Founded in 2009, the company is headquartered in Mountain View, USA, with a team of 1001-5000 employees. The company is currently Late Stage.

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