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Multimodal AI Research Engineer at Series B-backed AI research lab

Job in San Francisco, San Francisco County, California, 94112, USA
Listing for: Jack & Jill
Full Time position
Listed on 2026-08-04
Job specializations:
  • Software Development
    AI Engineer (Applied/Software), Machine Learning/ ML Engineer, Data Scientist
Salary/Wage Range or Industry Benchmark: 180000 - 260000 USD Yearly USD 180000.00 260000.00 YEAR
Job Description & How to Apply Below

Job Title

Multimodal AI Research Engineer

Salary

Not Disclosed

Company Description

Series B-backed AI research lab

Job Description

As a Research Engineer, you will develop state-of-the-art multimodal foundation models to power next-generation interactive digital interfaces. You'll focus on the intersection of audio, vision, and language to create low-latency, expressive systems. This role offers the unique opportunity to conduct high-level research while shipping impactful technology to a global user base at scale.

Location

San Francisco, USA;
London, UK; or Remote

Why this role is remarkable
  • Work on cutting-edge foundation models that bridge generative video and real-time interaction at the frontier of AI.
  • Join a well-funded Series B startup backed by premier global venture capital firms with a high-growth trajectory.
  • Transition research directly into production systems, seeing your work reach millions of users rather than staying in papers.
What You Will Do
  • Research and develop multimodal conversational models to control expressive digital behaviors in real-time with sub-second latency.
  • Fine-tune audio-visual foundation models to improve responsiveness, controllable expressions, and task-specific performance.
  • Partner with applied machine learning teams to migrate research prototypes into high-performance, scalable production pipelines.
The ideal candidate
  • PhD or equivalent research experience in Deep Learning, specializing in Large Multimodal Models or Generative AI.
  • Expert-level proficiency in PyTorch and experience building complex deep learning pipelines for audio or video understanding.
  • Proven track record of high-impact research, ideally with a history of publications at top-tier venues like NeurIPS, CVPR, or ICLR.
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