We’re a tight-knit team of passionate technologists building AI agents that help people design and make new products, services, and discoveries.
We get energized by solving challenging and meaningful problems, building useful and seamless products, and helping move the world forward.
Our team has published award-winning AI research, includes alumni from Caltech, and Professors from Caltech and UT Austin.
We're backed by top-tier investors and partners: Eric Schmidt (former CEO of Google and former Executive Chairman of Alphabet), Caltech, Jeff Dean (Chief Scientist, Google DeepMind and Google Research), and JP Millon.
We strive for excellence, focus, and impact.
We value thinking from first principles, learning fast, and getting things done.
We want to empower you to take ownership in fulfilling our mission.
Above all, we value team spirit, sharing the ups and downs, achieving great things together, and having fun while doing so.
Be a key team member that will help set the course, take ownership, and execute rapidly.
Design, train, and evaluate hybrid AI systems that perform well at scale and make optimal trade-offs.
Build data processing pipelines
Implement machine learning models
Run machine learning workloads at scale using distributed computing
Define and apply simple design principles that scale (Occam's Razor)
Solve min-max problems: how can we do more with less?
Accelerate our work by removing operational and tooling bottlenecks.
You enjoy and are energized by solving challenging and meaningful real-world problems.
You have a track record in a technical domain, e.g., machine learning, computer science, physics, math.
You have developed and implemented machine learning algorithms, models, and tools.
You have strong programming and math abilities.
You have clear verbal and written communication skills
You have strong conceptual and structured thinking.
You are willing and able to learn quickly.
You have team spirit.
You can independently structure, plan, prioritize, and get things done.
You have a drive for excellence, a sense of urgency, and bias to action.
Open-source projects, published research papers, or other examples of experience in using machine learning.
Experience with applying deep learning, reinforcement learning, unsupervised learning, and other techniques to large-scale problems.
Experience with distributed computing and handling large datasets.
Competitive salary
Stock options
100% covered premium health, dental, and vision insurance.
Wellness benefits (e.g., gym, fitness classes, physical therapy).
Retirement 401k: 100% match of your 401k deferrals up to 4% of your compensation.
Commuter benefits
Daily meals in the office
Training and development budget, e.g., for domestic conferences.
Flexible working hours
Unlimited PTO (with manager approval)
Team-building events and celebrations
Step 1: CS fundamentals assessment
To prepare for this, it would be good to have a solid understanding of basic data structures (e.g., lists, hash maps, stacks, queues, trees) and algorithms (e.g., sorting, depth-first vs breadth-first search, dynamic programming).
Step 2: Interviews
Optional: presentation on previous (research) projects.
2x machine learning interviews: 1:1 interviews where we will go over a machine learning problem in a collaborative code editor. The goal is to assess your current knowledge level of machine learning, relevant math/statistics concepts, coding, and general problem solving and communication skills.
1x CS / technical communication interview. A mix of coding and debugging/analyzing existing code.
1x discussion of behavioral cases and your career goals.
Step 3: Offer
Background and reference checks.
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