Deeproute.ai

Licenses autonomous driving systems to OEMs

Member of Technical Staff - Multimodal Foundation Models

AI ResearchConfirmed live in the last 24 hours

Full-timeFremont, CA, USANo salary listedIn-person

About the Job

Requirements

A Master of Science or PhD in Computer Vision, Machine Learning, Robotics, Computer Science, or a related field.
Strong understanding of foundation models, self-supervised learning, representation learning, multimodal learning, and large-scale pretraining.
Hands-on experience with methods such as CLIP, DINO or DINOv2, MAE, contrastive learning, masked modeling, and Mixture-of-Experts or scalable transformer architectures.

Responsibilities

Develop scalable pretraining pipelines for large-scale multimodal driving data.
Design and optimize training strategies for vision-language-action models, video foundation models, long-context temporal modeling, and multimodal representation alignment.
Improve training stability, data efficiency, scaling efficiency, and representation robustness.
Work on distributed training systems and large-scale model optimization using frameworks such as PyTorch Distributed, DeepSpeed, and Megatron-LM.
Design and improve self-supervised and multimodal learning methods for real-world autonomous driving systems.
Conduct architecture-level research on Vision Transformers, video and temporal architectures, multimodal fusion and alignment, embedding and retrieval systems, and long-context and memory-efficient architectures.
Explore and improve pretraining objectives, loss functions, training paradigms, generalization, and robustness.
Analyze model behavior through rigorous ablation studies, failure case analysis, representation probing, and evaluation.
Improve the efficiency, scalability, and deployability of large multimodal foundation models for real-world autonomous driving systems.
Work on model quantization, knowledge distillation, efficient attention mechanisms, sparse architectures and Mixture-of-Experts, long-context and memory-efficient modeling, inference acceleration and serving optimization, and training and inference system efficiency.
Optimize model throughput, latency, memory usage, and deployment performance for large-scale production environments.

Desired Qualifications

Experience with video foundation models, long-context modeling, retrieval systems, efficient inference, distributed training, and model compression and deployment optimization is highly valued.
A strong publication record in top-tier venues such as CVPR, ICCV, ECCV, NeurIPS, ICLR, or ICML is preferred.

Member of Technical Staff (MTS) - Multimodal Foundation Models

Deeproute.ai · Fremont, United States · — · Posted 2026-06-06

Workplace: on_site

Description

Focus

Multimodal Foundation Models · Representation Learning · Method Innovation

We are looking for strong technical builders and researchers who deeply understand foundation models and representation learning beyond simply applying existing frameworks.

Ideal candidates should have:

Strong experimental rigor
Solid systems and modeling intuition
Hands-on engineering ability
Interest in scalable multimodal AI systems for real-world autonomy

We value people who can bridge research and production, and who care about robustness, scalability, efficiency, and practical deployment in large-scale autonomous driving systems.

Responsibilities

1. Large-Scale Foundation Model Pretraining

Develop scalable pretraining pipelines for large-scale multimodal driving data
Design and optimize training strategies for:
Vision-language-action models
Video foundation models
Long-context temporal modeling
Multimodal representation alignment
Improve:
Training stability
Data efficiency
Scaling efficiency
Representation robustness
Work on distributed training systems and large-scale model optimization using frameworks such as:
PyTorch Distributed
DeepSpeed
Megatron-LM

2. Representation Learning & Method Innovation

Design and improve self-supervised and multimodal learning methods for real-world autonomous driving systems
Conduct architecture-level research on:
Vision Transformers (ViT)
Video / temporal architectures
Multimodal fusion and alignment
Embedding and retrieval systems
Long-context and memory-efficient architectures
Explore and improve:
Pretraining objectives
Loss functions
Training paradigms
Generalization and robustness
Analyze model behavior through:
Rigorous ablation studies
Failure case analysis
Representation probing and evaluation

3. Efficient Foundation Models & Scalable Deployment

Improve the efficiency, scalability, and deployability of large multimodal foundation models for real-world autonomous driving systems
Work on areas such as:
Model quantization
Knowledge distillation
Efficient attention mechanisms
Sparse architectures and Mixture-of-Experts (MoE)
Long-context and memory-efficient modeling
Inference acceleration and serving optimization
Training and inference system efficiency
Optimize model throughput, latency, memory usage, and deployment performance for large-scale production environments

Requirements

MS or PhD in:
Computer Vision
Machine Learning
Robotics
Computer Science
Related fields
Strong understanding of:
Foundation models
Self-supervised learning
Representation learning
Multimodal learning
Large-scale pretraining
Hands-on experience with methods such as:
CLIP
DINO / DINOv2
MAE
Contrastive learning
Masked modeling
MoE or scalable transformer architectures
Experience with one or more of the following is highly valued:
Video foundation models
Long-context modeling
Retrieval systems
Efficient inference
Distributed training
Model compression and deployment optimization
Strong publication record in top-tier venues is preferred:
CVPR
ICCV
ECCV
NeurIPS
ICLR
ICML

Apply

Apply at Deeproute.ai

About the Company

Deeproute.ai

Licenses autonomous driving systems to OEMs

Deeproute.ai develops smart driving solutions for the automotive industry, offering Level 2 ADAS and Level 4 autonomous driving for robotaxis and medium-duty trucks. Its DeepRoute-Driver and DeepRoute IO platform use an end-to-end model that combines perception, prediction, and planning, and it relies on map-free navigation that does not require HD maps. The company licenses its autonomous systems to OEMs on a per-vehicle basis and differentiates itself with map-free technology, a dual-track strategy, and partnerships with automakers like Dongfeng, Geely, Great Wall, and CaoCao Mobility. Its goal is to scale the installed base from thousands to about 200,000 vehicles by 2025, expanding through robotaxi services and fleet operations.

Company size: 51-200
Company stage: Series C
Total funding: $450M
Headquarters: Shenzhen, China
Founded: 2019

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