Research Scientist / Engineer, Pre-training


San Francisco | Permanent
DeepRec has partnered with a frontier AI research lab building new model architectures, not fine-tuning someone else's. Pre-training is the foundation every product and post-training effort builds on, and the team concentrates on a handful of large training efforts rather than spreading thin.


The Opportunity


You'll run controlled experiments on large models and let the data settle the arguments. You'll have the compute to test real hypotheses at scale, plus room to lead your own projects alongside the flagship runs.


What You'll Do

  • Research model architecture, training algorithms, optimisers and data pipelines
  • Lead small projects independently while contributing to flagship training runs
  • Design scaling-law experiments that set data, architecture and hyperparameter choices
  • Scale training across thousands of accelerators with sound fault tolerance
  • Build the dev tooling that keeps the research team moving quickly
  • Work across the stack, from kernel throughput to high-level model design
  • Design for downstream serving cost as well as model quality

What You'll Bring


Essential
  • MS or PhD in CS, ML or a related field
  • PyTorch distributed training, including FSDP and Megatron-style tensor or pipeline parallelism
  • Debugging loss spikes, divergence and throughput regressions at scale
  • Building and balancing data mixtures and tokenization pipelines
Desirable
  • Publications at NeurIPS, ICML, ICLR or similar venues
  • FP8 or other low-precision training
  • Custom CUDA or Triton kernels, or SSM-style and other non-standard architectures
We encourage you to apply even if you don't meet every requirement. The right mindset matters as much as the right CV.

 

Apply Now


If you want your experiments to shape the models themselves, we'd like to hear from you. Send your CV and we'll share the full brief in a confidential conversation.