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Gradient Learning Jobs in California (NOW HIRING)

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Sr Machine Learning Engineer

San Jose, CA

$65.25 - $86.50/hr

Strong knowledge of statistical and machine learning techniques, including but not limited to logistic regression, time-series modeling, random forests, support vector machines, gradient boosting (e ...

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$9

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How much do gradient learning jobs pay per hour?

As of Jun 9, 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 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.

How does a professional in Gradient Learning typically 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 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.
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What cities in California are hiring for Gradient Learning jobs? Cities in California with the most Gradient Learning job openings:
Gradient Learning jobs near you
Senior GenAI Research Engineer - Optimization and Kernels

Senior GenAI Research Engineer - Optimization and Kernels

Databricks

San Francisco, CA • On-site

$123K - $169K/yr

Full-time

This job post has expired today. Applications are no longer accepted.

Job description

Job Summary:
Databricks is a leading data and AI company focused on enabling data teams to tackle complex problems. As a Senior GenAI Research Engineer, you will drive performance improvements and design high-performance GPU kernels for large language model training while collaborating with a diverse team of researchers and engineers.
Responsibilities:
• Drive performance improvements through advanced optimization techniques including kernel fusion, mixed precision, memory layout optimization, tiling strategies, and tensorization for training-specific patterns
• Design, implement, and optimize high-performance GPU kernels for training workloads (e.g., attention mechanisms, custom layers, gradient computation, activation functions) targeting NVIDIA architectures
• Design and implement distributed training frameworks for large language models, including parallelism strategies (data, tensor, pipeline, ZeRO-based) and optimized communication patterns for gradient synchronization and collective operations
• Profile, debug, and optimize end-to-end training workflows to identify and resolve performance bottlenecks, applying memory optimization techniques like activation checkpointing, gradient sharding, and mixed precision training.
Qualifications:
Required:
• BS/MS/PhD in Computer Science or related field with hands-on experience writing and tuning CUDA kernels for ML training applications, or hands-on experience in distributed training frameworks (PyTorch DDP, DeepSpeed, Megatron-LM, FSDP)
• Strong understanding of NVIDIA GPU architecture (memory hierarchy, tensor cores, warp scheduling, SM occupancy) and proficiency with CUDA debugging/profiling tools (Nsight, NVProf)
• Deep understanding of parallelism techniques and memory optimization strategies for large-scale model training, with proven ability to debug and optimize distributed workloads
• Strong software engineering skills in Python and PyTorch, with experience supporting production training workflows and knowledge of LLM training dynamics including hyperparameter tuning and optimization strategies.
Company:
Databricks is a data and AI platform that unifies data engineering, analytics, and machine learning on a lakehouse architecture. Founded in 2013, the company is headquartered in San Francisco, USA, with a team of 5001-10000 employees. The company is currently Late Stage.

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