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ML Engineer; Multimodal & Gen AI

Job in San Francisco, San Francisco County, California, 94199, USA
Listing for: Rise Technical
Full Time position
Listed on 2026-06-15
Job specializations:
  • Engineering
    AI Engineer (Applied/Software), Artificial Intelligence
  • IT/Tech
    Machine Learning/ ML Engineer, AI Engineer (Applied/Software), Artificial Intelligence
Salary/Wage Range or Industry Benchmark: 170000 - 220000 USD Yearly USD 170000.00 220000.00 YEAR
Job Description & How to Apply Below
Position: ML Engineer (Multimodal & Gen AI)

Machine Learning Engineer (Multimodal / Generative AI)

$170,000 - $220,000 + Equity + Healthcare + 401(k) + PTO

Are you a Machine Learning Engineer looking to work on challenging computer vision and generative AI problems while joining a high-growth AI start-up developing next-generation multimodal technology?

This is an opportunity to join a fast‑growing AI company developing next‑generation multimodal systems that combine computer vision, generative AI, and large‑scale data pipelines. The team works on complex problems involving photorealistic image generation, visual understanding, and multimodal modeling.

In this role, you will develop and optimize machine learning systems across generative and vision‑based architectures. You will work closely with research and infrastructure teams to bring advanced AI models from experimentation into scalable production systems.

This role would suit an engineer who enjoys solving complex modeling problems while building production‑ready machine learning systems.

The Role:
  • Develop and improve computer vision and generative AI models
  • Optimize diffusion and multimodal ML pipelines for performance
  • Design experiments evaluating visual realism and consistency
  • Deploy scalable machine learning systems into production
  • Collaborate with research and infrastructure teams on model performance
The Person :
  • Strong programming skills in Python and machine learning frameworks
  • Experience with PyTorch or similar deep learning frameworks
  • Familiarity with CNNs, Vision Transformers, or diffusion models
  • Experience building or deploying machine learning systems
  • Strong understanding of machine learning fundamentals
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