Career | Machine Learning Engineer - LLM Specialist | Encultured AI

Machine Learning Engineer - LLM Specialist

Encultured AI

Berkeley, CA, US / San Francisco, CA, US

  • Job Type: Full-Time
  • Function: Data Science
  • Industry: Consumer Software
  • Post Date: 11/16/2023
  • Website:
  • Company Address:
  • Salary Range: $1 - $1

About Encultured AI

Encultured AI is a video game company focused on enabling the safe introduction of AI technologies into our game world.

Job Description


Berkeley / SF bay area. We work in-person at our office in Berkeley for two days per week, and on other days we have the option of working from the office or from home. Fully remote work may be an option for very strong candidates with years of experience in game development.


Starting between $120k and $180k per year depending on experience, plus healthcare benefits, and equity incentives vesting over 5 years, with raises also becoming available with good individual performance or team-wide accomplishments that expand our revenue stream. We also offer a Safe-Harbor 401(k) with the IRS maximum employer matching.


In this role we need candidates to have experience with building and training large language models (LLMs).

In addition, we have two tiers of qualification for this role: Junior ML Engineer and Senior ML Engineer. To qualify directly for the Senior title upon joining, we require candidates who have received a Bachelor’s degree or above in computer science, physics, mathematics, or a closely related field.

These are not required, but our team will welcome and make good use of experience with:
  • building and training reinforcement learning algorithms and/or environments,
  • applying and developing AI alignment methods,
  • research on humans and human culture, including but not limited to background in the humanities, social sciences, cognitive science, and biology,
  • machine learning interpretability research and tools,
  • PhD-level research and writing.

We use cookies to customize your user experience. Click “Agree” if you agree with our Policy.