Summary
At Imbue, we care deeply about our team members' personal and individual growth. We’re looking for a special type of Engineering Manager who cares about this as much as we do. This role is less about managing deadlines or schedules and more 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 reviews, providing feedback, 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.
Responsibilities
• Mentoring other engineers, especially junior team members, through one-on-ones and pairing sessions.
• Unblocking individuals. You will be responsible for ensuring that anything that gets in the way of other engineers gets resolved, whether it is a personal issue or a technical one.
• Performing code reviews and providing feedback so that other engineers can continually hone and improve their craft.
• Conducting lots of technical interviews.
• Onboarding new team members, improving that process, and ensuring that new team members feel welcome, safe, and included.
• Spending time with team members to help them determine what is important, what they should work on, and what their areas for growth are.
You are
• Highly empathetic. We like everyone to feel comfortable and able to contribute, and you will be a key contributor to that feeling of safety.
• An experienced software developer from whom other engineers are excited to learn.
• Interested in people and culture. You are excited about using and investing in the softer skills that management requires.
• Knowledgeable about machine learning. You don’t need to be a world-famous published researcher, but you should be comfortable with training deep neural networks and much of the latest machine learning theory and practice.
Compensation and Benefits
• Work directly on creating software with human-like intelligence.
• Generous compensation, equity, and benefits.
• $20K+ yearly budget for self-improvement: coaching, courses, conferences, etc.
• Actively co-create and participate in a positive, intentional team culture.
• Spend time learning, reading papers, and deeply understanding prior work.
• Frequent team events, dinners, off-sites, and hanging out.
• Compensation packages are highly variable based on a variety of factors. If your salary requirements fall outside of the stated range, we still encourage you to apply. The range for this role is $200,000–$450,000 cash, $10,000–$2,500,000 in equity
How to apply
All submissions are reviewed by a person, so we encourage you to include notes on why you're interested in working with us. If you have any other work that you can showcase (open source code, side projects, etc.), certainly include it! We know that talent comes from many backgrounds, and we aim to build a team with diverse skillsets that spike strongly in different areas.
We try to reply either way within a week or two at most (usually much sooner).
Learn more about our full interview process here.
About us
Imbue builds AI systems that reason and code, enabling AI agents to accomplish larger goals and safely work in the real world. We train our own foundation models optimized for reasoning and prototype agents on top of these models. By using these agents extensively, we gain insights into improving both the capabilities of the underlying models and the interaction design for agents.
We aim to rekindle the dream of the *personal* computer, where computers become truly intelligent tools that empower us, giving us freedom, dignity, and agency to pursue the things we love.
Generally Intelligent is an AI research company. We help machines learn to understand the world the way humans do. Our mission is to build human-like general machine intelligence and make it safely accessible in order to foster a more abundant, equitable, and creative human society.
We take a first-principles approach to understanding the fundamentals of learning, starting with simple self-supervised networks solving early evolutionary problems, and increasing complexity incrementally.