Multimodal ML Research Intern: Omni-Model Innovation

Stfrancis; Palo Alto (CA); On-site; USD 80,000 - 125,000; Full time

Benefits offered by this job

Paid sick leave

Up to 13 paid holidays annually

Company-sponsored medical plan (full‑/

Job summary

Stfrancis is actively researching and developing Omni multimodal large models, including training data design, algorithm design for foundational models, and optimized pre-training, SFT, and RL workflows. The work spans evaluation of capabilities and exploration of downstream applications to stay at the forefront of multimodal AI.

The team emphasizes scientific problem analysis, identifying bottlenecks, and applying first-principles solutions to accelerate progress while maintaining cutting-edge

Qualifications

• Bachelor’s degree in a relevant field; graduate degrees prioritized.
• Hands-on experience in large-scale multimodal data processing and high-quality data generation.
• Strong foundation in deep learning algorithms and practical experience in large model development; familiarity with Diffusion Models and Autoregressive Models is advantageous.
• Publication in top-tier conferences or experience in cross-modal research (e.g., audio-visual) preferred.
• Proficiency in deep learning network implementation, model tuning for training/inference, CPU/GPU acceleration, and distributed training/inference optimization; practical experience is a plus.
• Participation in ACM or NOI competitions is highly valued.
• Strong learning agility, communication skills, teamwork, and curiosity.

Responsibilities

• Conduct research and development of Omni multimodal large models, including training data design, foundational model algorithm design, optimization of pre-training/SFT/RL, model capability evaluation, and exploration of downstream applications.
• Analyze R&D challenges scientifically, identify performance bottlenecks, and develop solutions based on first principles to accelerate model development and maintain competitive, leading-edge performance.
• Explore diverse paradigms for Omni-modal understanding and generation, research next-generation model architectures, and advance multimodal model capabilities.

Skills

Multimodal data processing

Deep learning foundations

Model training/tuning

Diffusion models

Autoregressive models

Education

Bachelor’s degree (full-time preferred) or higher in Computer Science, AI, Mathematics, or related fields

Job description

Stfrancis is actively researching and developing Omni multimodal large models, including training data design, algorithm design for foundational models, and optimized pre-training, SFT, and RL workflows. The work spans evaluation of capabilities and exploration of downstream applications to stay at the forefront of multimodal AI.

The team emphasizes scientific problem analysis, identifying bottlenecks, and applying first-principles solutions to accelerate progress while maintaining cutting-edge