Level

Hume AI

AI Researcher

Hume AI is hiring an AI Researcher for a remote role open to applicants in United States. It pays $180k-$350k a year and Level rates it ; you can apply on Hume AI.

Level's read on this job

  • On Level's scale, this role is Builds AI ●●●●: the job is building AI systems.
  • This is listed as a remote role.
  • The posting mentions PyTorch, fine tuning and AI research among its tools and skills.
  • Level classes it as a mid-level role.
  • Hume AI has 4 other open roles on Level.

AI in this role

pytorch
fine-tuningai-research

Hume AI is seeking talented AI researchers interested in working with our team to build state-of-the-art speech-language models (SLMs). Our new SLM training method—reinforcement learning from human expression (RLHE)—learns human preferences from behavior in millions of audio and video recordings, making SLMs superhumanly helpful, interesting, funny, eloquent, honest, and altruistic. Join us in the heart of New York City and contribute to our endeavor to ensure that AI is guided by human values, the most pivotal challenge (and opportunity) of the 21st century.


About Us

Hume AI is a Series B startup dedicated to building artificial intelligence that is directly optimized for human well-being. As the first company to release speech language models, we’re focused on expanding our research to encompass audio understanding models and evaluation platforms for enterprises.

Our goal is to enable a future in which technology draws on an understanding of human emotional expression to better serve human goals. As part of our mission, we also conduct groundbreaking scientific research, publish in leading scientific journals like Nature, and support a non-profit, The Hume Initiative, that has released the first concrete ethical guidelines for empathic AI (www.thehumeinitiative.org). You can learn more about us on our website (https://hume.ai/) and read about us in WIRED, Forbes, and Venturebeat.

About the Role

We are looking for talented researchers with relevant publications, 2+ years of experience in a research role in industry, and/or a proven track record of contributions to open source language, audio, or video models. (A PhD is neither necessary nor sufficient.) We're looking for someone excited to work with our team to build state-of-the-art audio models and evaluation tools. In this role, you will build out software systems that support distributed model training, inference, and benchmarking as well as massive-scale data collection, storage, preprocessing, and analysis. You will be working to solve some of today’s most exciting AI research problems at industry scale.

 

Requirements

  • Expertise in the Python ecosystem and popular ML libraries and tools (e.g. PyTorch)

  • Experience writing robust and maintainable production-ready code

  • Comfort iterating quickly on new and uncertain research directions

  • 2+ years of experience contributing to the training and/or fine-tuning of models with large-scale datasets of text, audio, image, and/or video data (predictive or generative)

Application Note

Please apply only to the position that best aligns with your qualifications. If you submit multiple applications or have applied within the past 6 months, only your initial submission will be considered.

Annual Salary$180,000—$350,000 USD

How we rate this

AI Researcher at Hume AI rates 96 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Fine-tuningAI ResearchPyTorch

Questions you could be asked

  1. Walk me through fine-tuning a model: what data did you use, and how did you check the result?
  2. Tell me about a research question you investigated. What did you find?
  3. What are the limits of PyTorch that you've run into, and how did you work around them?
  4. How would you decide a model or AI system is ready to ship?
  5. Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

  • List these exact terms on your resume: Fine-tuning, AI Research, and PyTorch. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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