Machine Learning Engineer — Pre-training (LLM)
$150k - $450k • Sunnyvale, CA • Full-time
Posted 12d ago
Job Location
Sunnyvale, CA
Tech Stack
Remote Work Policy
On-site
Employment Type
Full-time
Categories
Machine Learning Engineer
About the job
We are a dedicated research lab focused on building, understanding, using, and risk-managing foundation models. Our mission is to advance research, nurture the next generation of AI builders, and drive transformative contributions to a knowledge-driven economy. As part of our team, you will have the opportunity to work on the core of cutting-edge foundation model training alongside world-class researchers, data scientists, and engineers. You will tackle fundamental and impactful challenges in AI development, participating in the creation of groundbreaking AI solutions that have the potential to reshape entire industries. Strategic and innovative problem-solving skills are essential for establishing our institution as a global hub for high-performance computing in deep learning.
Responsibilities
- Extend or modify training frameworks like DeepSpeed or FSDP to support new use cases and architectures.
- Translate mathematical optimizer specifications into distributed implementations.
- Create and debug multi-node launch scripts with flexible batch sizes, parallelism strategies, and hardware targets.
- Select and validate data, tensor, pipeline, expert, and context parallelism strategies for models and clusters.
- Build systems for experiment tracking, job monitoring, and logging for collaborators and researchers.
- Write production-quality code and tests for ML infrastructure in PyTorch or JAX, ensuring reliability and maintainability at scale.
- Build and maintain data-loading and checkpoint/restart workflows, restoring model, optimizer, RNG, and data progress after interruptions.
Requirements
- 5+ years of experience in ML systems, infra, or distributed training.
- Experience modifying distributed ML frameworks (e.g., DeepSpeed, FSDP, FairScale, Horovod).
- Strong software engineering fundamentals including Python, systems design, and testing.
- Proven multi-node experience (e.g., Slurm, Kubernetes, Ray) and debugging skills (e.g., NCCL/GLOO).
- Ability to implement algorithms across GPUs/nodes based on mathematical specifications.
- Experience working on an ML platform/infrastructure or distributed inference optimization team.
- Experience with large-scale machine learning workloads and strong ML fundamentals.
- Experience with large-scale pre-training.
- Exposure to mixed-precision training (e.g., bf16, fp8) with accuracy validation.
- Familiarity with performance profiling, kernel fusion, or memory optimization.
- CUDA or Triton kernel experience.
- Experience building custom training pipelines at scale and modifying them for custom needs.
- Deep familiarity with training infrastructure and performance tuning.