The VLM team builds vision-language models that run on-device, under tight latency and memory constraints, without sacrificing quality.
Join a focused, hands-on group that works directly on models and collaborates closely with our pretraining, post-training, and infrastructure teams.
Lead a new model capability end-to-end from task spec through data curation, training recipe, ablations, evaluation, and into the final shipped model.
Improve visual reasoning through reinforcement learning and preference optimization methods.
Push the quality-efficiency frontier on token efficiency via encoder/connector design.

Hands-on experience in training or evaluating VLMs with demonstrated experimental rigor.
Ability to turn research ideas into scalable implementations, refine and iterate through hypotheses.
Proficiency in Python and at least one deep learning framework.
M.S. or Ph.D. in Computer Science, Mathematics, or a related field; or equivalent industry experience.
Building or optimizing multimodal training or data pipelines.
Experience with distributed training (DeepSpeed, FSDP, Megatron-LM, etc.).
Multimodal post-training experience (SFT, preference optimization, RL-style methods).
Dataset design and data quality expertise (quality and diversity assessment, long-tail mining).
Prior open-source contributions (code, data, models) on GitHub or Hugging Face.
Published research at top AI conferences (NeurIPS, ICML, CVPR, ECCV, ICLR, ACL, etc.).
Experience with computer vision or visual representation learning.

Full ownership: You own your work from architecture to deployment.
Compensation: Competitive base salary with equity in a unicorn-stage company
Health: We pay 100% of medical, dental, and vision premiums for employees and dependents
Financial: 401(k) matching up to 4% of base pay
Time Off: Unlimited PTO plus company-wide Refill Days throughout the year

Member of Technical Staff – Multi-Modal, Vision

Key skills