About The Role

You'll work at the core of LambdaQ: training the foundation models that power every Vikasit product. From data curation and tokenization to large-scale distributed training and post-training, you'll push our models up the frontier while keeping inference economics sane.

What you'll do

—Own parts of the pretraining pipeline — data, architecture, or training infrastructure
—Run and analyze large-scale training runs on multi-node GPU clusters
—Improve data quality, mixtures, and curricula for Indian and global use
—Design experiments that move benchmark and downstream quality measurably

What we're looking for

—Robust PyTorch and distributed-training experience (FSDP / DeepSpeed / Megatron)
—Solid grasp of transformer internals, optimization, and scaling laws
—Experience training models at 1B+ scale, or equivalent research depth

Nice to have

—MoE training experience
—Tokenizer / data-pipeline work
—Publications at top ML venues

Sound like you?

We hire for skill over credentials. Tell us why you're a fit — links and projects welcome.

Apply for this role

📌 Member of Technical Staff, Pretraining (Pune)
🏢 LambdaQ labs
📍 Pune