We understand that different environments work better for different people. Here, we have two flavors of role: in-person at our office in San Francisco and remote from anywhere you can be effective. The two roles differ in nature and each support their own range of lifestyles.
At our office in SF, we believe in the power of getting people in the same room, debating tough topics, and having fun together on a regular basis. We have a daily team standup to share our work, unblock progress, and have a few laughs together.
We work hard to create an environment of psychological safety so people feel empowered to explore their ideas—even the crazy ones. We protect deep work time so that people have space to innovate and create new value for our team, company, and society.
We share feedback (especially positive feedback!) frequently so you can better understand the unique value you bring to the table and hone your superpowers. We encourage self-improvement by supporting experimentation and rapid iteration on your own habits and working norms.
Our remote engineers have the freedom to set their own location and hours. We expect them to communicate clearly and fulfill their obligations to the team while enjoying plenty of flexibility in exactly how and when they do their work and live their lives.
We record important information from all meetings so that others can catch up asynchronously if they want. We structure our communications so that you never need to read through long docs or Slack threads to figure out what's going on.We want you free to spend most of your time doing what you do best: engineering elegant solutions.
This position is great for people who love to code, and who love to write high quality software. We do not have customers, and therefore, we rarely have hard deadlines which force us to ship mediocre code. We prefer to write tests, use linters and type checking, and create code that is a joy for others to work with, and we're willing to put in the time it takes to do so.
Whether you’re remote or in-person, we utilize the mutual understanding we fostered during the interview stage to help you shape your role based on your skills, interests, and ideas. As the final round of our interview process, we do our best to present you with a trial project that is “up your alley” in some sense so you can see how your unique skillset may apply to our work.
We believe in fostering mutual understanding and alignment through our interview process. We ask that you be transparent with us so we can understand you as a whole person—from exploring your deeper goals, values, and motivations to emulating what it’s like to collaborate with you. In return, we proactively offer you the information you need to understand the full picture of life at Generally Intelligent.
For our engineering roles, we don’t require any hands-on ML/AI experience—we look for capable engineers who are quick and eager learners. For our research positions, we welcome people from unconventional backgrounds (including but not limited to physics, neuroscience, psychology, policy, etc.) and value the importance of diversity of thought and background. We do not require any advanced degrees.
Generally Intelligent was founded with the vision of fostering a more abundant and equitable society through the deployment of safe, generally capable agents. We believe that practical methods, strategies, and policies for safety need to be part of the design process from the very beginning.
At Generally Intelligent, we care deeply about our team members personal and individual growth. This role is about supporting and enabling our engineers to grow in the ways that they are excited to grow. You will be focused on mentoring other developers, performing code review, pair programming, and generally unblocking people so that they can do their best work. You will help onboard new hires, conduct interviews, and develop cultural best practices to create a truly world-class team.
At Generally Intelligent, our remote team is highly independent. This role is about conducting research independently into subjects that are of particular interest to us. Your sole responsibility will be to advance the frontier of human knowledge in a particular area that you are excited about.
At Generally Intelligent, we leverage large amounts of compute to make our small research team more effective. This role is about enabling and supporting those large-scale compute efforts and all of the other software infrastructure that goes into making research a pleasant, seamless experience for the rest of the team, especially as we scale to increasingly higher scale systems.
As a remote machine learning engineer, you’ll work very closely with a senior member of our research team on cutting-edge deep learning research, infrastructure, and tooling towards the goal of creating general human-like machine intelligence.
In this role you’ll work with our researchers to do cutting-edge deep learning research—conducting experiments, creating infrastructure, and developing tooling & visualizations—with the goal of developing more human-like machine intelligence.
This role is about supporting and promoting our open source projects. When possible, we like to release our software in a way that makes it broadly accessible to the wider research community (ex: our Avalon project). This role is about ensuring that we're able to rapidly respond to issue on those projects, developing them in ways that are most beneficial to the broader community, and ensuring that we have the bandwidth to open source future projects as well.
This role is about investigating the fundamental questions of intelligence, knowledge and understanding in order to develop software with human level intelligence. You will collaborate internally and externally with other researchers, and be supported by a team of research engineers.
Much of the work we do at Generally Intelligent is effectively pure software engineering. Our perspective is that even machine learning research ends up being about 90% software engineering, so even without any prior machine learning knowledge, there is plenty to contribute as a normal software engineer. Even most of our machine learning research tends towards the software engineering side of the spectrum, as we prefer to automate the types of work that academic researchers typically do (ex: tuning hyperparameters, experimenting with small variations in network architectures, etc).
In order to develop systems with more human-like intelligence, formulating the right tasks and training in an environment that leads to generalizable intelligence is of the utmost importance. As a starting point, we have built Avalon, an open-source, open-world 3D environment for training AI agents in a setting that is more similar to the environment in which humans evolved. In this role, you will iterate on and improve Avalon so that we(and other researchers) may use it as a benchmark for AI capabilities.
As a systems engineer, you’ll work on pioneering machine learning infrastructure that enables running large numbers of experiments in parallel across local and cloud GPUs, extremely fast training, and guarantees that we can trust experiment results. This allows us to do actual science to understand, from first principles, how to build human-like artificial general intelligence.
One of the main directions of research we care about at Generally Intelligent is developing better theoretical models and understanding of learning, optimization, memory, and agents. In particular, we're interested in the intersection of this theoretical knowledge and what that means for our day-to-day engineering of more general agents. This position is about developing the software to support those experiments and explorations.