# Senior GenAI Research Engineer - Optimization and Kernels
**Company:** [Databricks](https://hotfix.jobs/companies/databricks)
**Location:** San Francisco, CA
**Salary:** $166K-$225K
**Skills:** CUDA, PyTorch, Nvidia Gpu, Deepspeed, Megatron-Lm, Fsdp, Nsight, Nvprof, Distributed Training, Kernel Optimization
**Posted:** 2026-01-30
> Develops advanced optimization techniques and high-performance GPU kernels for scaling LLM training. Requires expertise in CUDA, PyTorch, distributed frameworks, and NVIDIA GPU architecture.
## Job Description
## Job Description

As a research engineer on the Scaling team, you will advance the scientific frontier in deep learning by creating new optimization techniques beyond the state of the art. You will collaborate with researchers and engineers to encode scientific expertise into products for customer success with state-of-the-art LLMs and AI systems.

## The Impact You Will Have
- 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

## What We Look For
- 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
**Apply:** https://hotfix.jobs/jobs/senior-genai-research-engineer-optimization-and-kernels-at-databricks-9ff9ea68-50cd-4ad8-bb06-ca1fc3b99a31
**Canonical:** https://hotfix.jobs/jobs/senior-genai-research-engineer-optimization-and-kernels-at-databricks-9ff9ea68-50cd-4ad8-bb06-ca1fc3b99a31