Senior Machine Learning Research Engineer

AI Talent Now job # ZR 104

We are looking for a Machine Learning Research Engineer with 5+ years of experience (PhD + 1 year industry, or 5 years industry with publications) to own the full lifecycle of ML research and development at the frontier of audio AI. You'll join a fast-growing team backed by NVIDIA and top-tier investors, building cutting-edge speech and audio models that power the data layer for the world's leading AI labs. This is a role for someone who thrives in a research-driven environment, has published at top conferences, and is excited to take end-to-end ownership of novel ML directions — from design and training to deployment and fine-tuning.

What will you be doing?

• Owning full research directions end-to-end — designing, training, deploying, and fine-tuning ML models for speech and audio applications
  
• Publishing and advancing the state of the art in speech, audio, and multimodal ML (NeurIPS, ICML, ICLR caliber work)
  
• Building production-grade inference systems and resilient pipelines that process terabytes of audio data daily
  
• Collaborating cross-functionally with operations and engineering teams to gather training/evaluation datasets and improve model quality
  
• Setting ML roadmaps and mentoring other engineers as the team scales
  

Requirements

Must have
Work experience:

1+ years in industry with full lifecycle ML ownership: design, train, deploy, fine-tune

Education:

PhD from a top-25 CS program (or 5+ years industry ML research experience without PhD)

Baseline
Seniority:

5 - 15 years of experience in ML research engineering, with focus on speech/audio/multimodal models using Python and PyTorch

Work experience:

Experience at a high-growth startup

Hard skills:

Research in speech, audio, multimodal, text-to-speech, or related domains

Strong Python and PyTorch with production deployment experience

Soft skills:

Excited to own a research direction end-to-end independently

Miscellaneous:

Based in or willing to relocate to San Francisco (in-office)

At least 1 year of industry experience (post-PhD)

Nice-to-have
Work experience:

Experience at a top AI lab or research-focused startup (e.g., DeepMind, xAI, Anthropic, OpenAI, Meta FAIR)

Mix of big company and startup experience

Education

Many publications, especially at NeurIPS, ICML, or ICLR in speech, audio, multimodal, or related areas

UpdatedHard skills

Experience training large neural network models