This role is a unique opportunity to get into cutting edge deep learning—no prior machine learning specific experience is required. We really care about personal growth (ex: the whole engineering team worked through Pieter Abbeel’s CS294 last year, including all the homeworks, and some of us also completed CS287 and CS285).
Salary: 130K - 170K
We’re looking for someone who is excited about the chance to build infrastructure for other deep learning researchers (ex: experiment tracking tools, model debugging methods, automated hyperparameter optimzers, developer tooling, etc).
- Gain real exposure to deep learning research. Unlike "machine learning" roles at most companies, this role is 100% focused on supporting deep learning research (not just productionizing someone else's system).
- Take time to develop a deep understanding of what makes neural networks actually work by reading papers and the underlying code. Ex: we have a weekly paper club where we each present a paper we’ve read recently and answer everyone’s questions about it.
- Be a part of a tight-knit team of great machine learning engineers / researchers. We all worked together at a previous startup, and have continued working together for years now because we love the culture we’ve built!
- Collaborate closely, even while remote. We’ve put an enormous amount of thought and effort into building an amazing remote-first culture. Ex: we spend at least half of our days pair programming, and we’re all in Gather.town every day so that it’s really easy to have quick, informal conversations.
- Work with other great programmers who care about their craft. Ex: even our machine learning code has tests (though we’re not dogmatic—they’re only tested to the extent that tests are useful). We have deterministic formatting, zero linter errors, wide type coverage with MyPy, etc.
- Are you empathetic, driven, and intellectually curious?
- Do you enjoy collaborating with other developers? Are you passionate about building tools for other programmers?
- Are you a great software engineer? We’re flexible on the exact number of years of experience, but this likely is not a great fit for those with fewer than 3 years of post-college work experience (or the equivalent—we love people who are self-taught).
- Are you very comfortable writing Python?
- Do you really care about making a great research culture that feels welcoming and helps people feel safe exploring new ideas and asking important questions? Our goal is to build the best research culture in the world, and everyone is expected to actively contribute to that. We love exploring new ways to collaborate more effectively, whether it’s using new tools for thought to organize information, or just something simple like new conversational habits and norms.
- Create world-class deep learning research infrastructure and tooling. Ex: we made a simple hyperparameter optimizer that allows us to tune models without spending any brain cycles or tons of compute. We love tools that free us up to work at a higher level.
- Build new features that make experimentation easier. Ex: automated checks for vanishing or exploding gradients and other obvious problems.
- Open source the best parts of our internal tooling and maintain our existing repositories. Ex: Jupyter Ascending, a tool we made that allows you to easily write code in a real editor, like vim or emacs, and instantly sync that into a Jupyter notebook, giving sort of the best of both worlds. Fixing bugs and resolving issues for the broader software community is important to us.
- Contribute patches to fix bugs in other open source repositories.
- Make our experiment infrastructure more robust. Ex: better handling for machines being killed in a distributed setup, or enabling experiments to be run easily on multiple cloud providers or even a local machine, etc.
Generally Intelligent is an early-stage AI research company. We’re working directly on building human-level general machine intelligence that can learn naturally in the that way humans do. Our mission is to understand the fundamentals of learning and build safe, humane machine intelligence.
We’re supported by investors that include Y Combinator, researchers from OpenAI, the founders of Dropbox, Lightspeed Venture Partners, and Threshold Ventures (formerly DFJ).