Member of Technical Staff (MTS) - Multimodal Foundation Models

Deeproute.ai; Colorado; On-site; USD 90,000 - 120,000; Full time

Job summary

Deeproute.ai is seeking a research scientist to advance multimodal representation learning for autonomous driving. The ideal candidate will engage in method-level innovation, designing new learning paradigms and training strategies for large-scale models. Strong academic credentials in Computer Science or related fields and experience with modern frameworks are essential. Responsibilities include developing novel methods, innovating on architectures, and collaborating across teams to implement new solutions. This is a unique opportunity to significantly impact autonomous driving technology.

Qualifications

• Strong academic background in Computer Science, AI, or related fields.
• Solid understanding of modern representation learning frameworks like CLIP and DINOv2.
• Proven experience in method-level innovation such as designing new loss functions.

Responsibilities

• Design and develop novel representation learning methods.
• Innovate on model architectures like Vision Transformers and Video models.
• Collaborate with engineering and data teams to integrate new methods.

Skills

Strong academic background in Computer Science, AI, or related fields

Solid understanding of modern representation learning frameworks

Experience in method-level innovation

Creating new training strategies

Education

Ph.D. in Computer Science, AI, or a related field

Tools

DeepSpeed

Megatron-LM

Job description

We are looking for a research scientist to push the frontier of multimodal representation learning for autonomous driving. You will focus on method-level innovation, including designing new learning paradigms, architectures, and training strategies for large-scale vision and video models.

This role is ideal for candidates who care deeply about how models learn, not just how to train them.

Responsibilities

1. Design and develop novel representation learning methods, including:
   • Self-supervised learning
   • Contrastive learning
   • Masked modeling
   • Teacher‑student / distillation frameworks
2. Innovate on model architectures, including:
   • Vision Transformers (ViT)
   • Video models (e.g., Video Swin)
   • Multimodal fusion architectures
3. Develop new training paradigms and loss functions to improve representation quality
4. Drive measurable improvements in:
   • Representation quality (e.g., linear probe, retrieval metrics)
   • Downstream performance (e.g., perception / E2E driving systems)
5. Collaborate with engineering and data teams to integrate new methods into production systems

Core Requirements

1. Strong academic background in Computer Science, AI, or related fields
2. Solid understanding of modern representation learning frameworks:
   • CLIP
   • DINOv2
   • Masked Autoencoders
3. Proven experience in method-level innovation, such as:
   • Designing new loss functions
   • Modifying architectures
4. Creating new training strategies

Preferred Qualifications

1. Publications in top‑tier venues:
   • CVPR
   • ICCV
   • NeurIPS
   • ICLR
2. Experience training models at scale using:
   • DeepSpeed / Megatron‑LM
3. Strong intuition for why models behave the way they do, not just how to train them