Research Systems Engineer

Applied Compute•San Francisco, CA

9d•Onsite

About The Position

As a research systems engineer, you'll train frontier-scale models and develop the methods that make continual learning work inside enterprise environments. You'll design and run experiments at scale, explore cutting-edge RL techniques, and build the tools that let us understand what's actually happening during training. This role sits at the intersection of research and systems. You'll invent new algorithms alongside researchers, then work with infrastructure engineers to run them on GPUs.

Requirements

Experience training or serving large language models
Experience building RL environments and evaluations for language models
Proficiency in PyTorch, JAX, or similar ML frameworks, with experience in distributed training
Strong experimental design skills — you know how to set up experiments that actually answer questions

Nice To Haves

Background in pre-training or post-training research
Previous experience in high-performance computing environments or large-scale clusters
Contributions to open-source ML research or infrastructure
Demonstrated technical creativity through published research, OSS contributions, or side projects

Responsibilities

Post-train frontier-scale language models on enterprise tasks and environments
Explore and develop RL techniques, co-designing algorithms and systems
Contribute to Alchemy, our data research program for generating signal-rich training environments from production data
Build high-performance internal tools for probing, debugging, and analyzing training runs
Partner with infrastructure engineers to scale training and inference efficiently