Most AI is frozen in place - it doesn’t adapt to the world. We think that’s backwards. Our mandate is to build efficient intelligence that evolves in real-time. We bring together exceptional people with the drive and creativity to push the boundaries of continual adaptation. We’re looking for builders and creative thinkers ready to shape the next era of intelligence.
Our team is small, talent-dense, and moving quickly, with a culture rooted in accountability, initiative and shared success. We look for people who care deeply, ship fast, and are eager to make a dent in the world. We operate with low egos, high velocity, and extreme ownership to build technology that expands access and benefits the many, not the few.
Impact over activity: We are building a generational company. We value people who execute on ideas rather than comment from the sidelines. We believe our competitive edge lies in caring about excellence across the entire lifecycle - from research to product. No task is beneath anyone: no one is too senior to test the build, clean the data, or unblock a teammate.
Vulnerable, low-ego collaboration: We believe exceptional work comes from people who challenge, support and push one another to think bigger. Rapid iteration requires stepping into discomfort. We encourage bold, half-baked, and unorthodox ideas. When we disagree, we share feedback directly, never through side channels.
Celebrate decisively: Extraordinary effort should be deeply memorable. We celebrate milestones generously and publicly - via spot bonuses, team dinners, spontaneous gifts, and company-wide recognition - to fuel each other for the next sprint.
Our vision is AI systems that are flexible, adaptable, and accessible to everyone. We believe efficiency is what makes this possible. It’s how we expand access and ensure innovation benefits the many, not the few.
This is a research role which is focused on real world impact. We care about building our frontier innovation around efficiency, gradient free exploration, real time learning and interface design. A revolution is underway where the cost of generating synthetic data is now low enough that we can treat the data space as malleable and something which can be optimized. We can steer synthetic data towards desirable properties and make previously invisible worlds with limited data coverage more visible.
The most intelligent system will increasingly be defined by building an algorithm that can interact with the world. This means for the first time researchers who care about intelligence need also be obsessed with how a model interacts. If any of these statements resonate with you, we would like to hear from you.
Our research principles
Sweat the details. Technical excellence requires obsessing over every detail. We co-design serving, algorithms, and interface as one system to maximize efficiency and enable real-time adaptation.
Move with conviction. Extraordinary results require extraordinary effort. We operate with urgency as a unified team, concentrating resources on a few high-conviction bets where research and impact intersect.
Metrics that matter. The most intelligent system will increasingly be defined by building an algorithm that can interact with the world. Research ideas are tested through working products. If it doesn't improve what users can do, we question whether it matters. We share what moves the field; we don't optimize for paper count.
Innovation: innovate on our product to co-design algorithms that react real-time to product signal and feedback. Design new ways of giving feedback that drive better algorithms.
Cross-stack optimization: collaborate across software, hardware, and algorithmic domains to achieve system-wide efficiency gains
Measure what matters: we believe strongly that the ultimate signal of value is whether we have real world impact. Our current algorithms are capable of interacting with the world which is why product matters so much.
We look beyond traditional markers of experience. What matters most to us is what you’ve built and the impact you’ve had. We value demonstrable output, curiosity and alignment with our core values:
A PhD or equivalent research experience in a computer science field
4-5+ years of industry experience
Deep expertise in at least one area: model efficiency, real time alignment or algorithmic optimization
Systems thinking ability to understand and optimize across the full ML stack
Strong programming skills in Python. Experience with deep learning frameworks (PyTorch, JAX, TensorFlow)
Knowledge of model optimization techniques (RLHF, finetuning)
Experience in an industry lab with computing at scale
Above all, we're looking for great teammates who make work feel lighter and aren't afraid to go out on a limb with bold ideas. You don't need to be perfect, but you do need to be adaptable. We encourage you to apply, even if you don't check every box.
Innovative culture: You’ll be part of a generational opportunity to define the trajectory of AI, surrounded by a team pushing the boundaries of what’s possible.
Growth paths: A dynamic environment with opportunities to take on new challenges, expand your responsibilities, and make an impact beyond your immediate role.
Adaption Passport: An annual travel allowance to explore somewhere new, experience different cultures and keep expanding your horizons. It can be used to travel to any country you haven’t previously been to.
Lunch on us: A weekly allowance for takeout or grocery delivery, wherever you are in the world. Whether it's stocking your fridge or having dinner delivered, it’s one less thing to think about.
Comprehensive benefits: High-quality medical coverage and benefits designed to support your health and well-being.
Team offsites and visits to SF HQ: We're a global team, but believe there's real value in spending time together in person. We regularly bring teammates together at our SF HQ and for team offsites in locations around the world, including London, Mexico City, and New York.
Combine life sciences research expertise with hands-on Python skills to create and review rigorous scientific problems, analyze biological datasets, validate computational results, and improve advanced AI systems.
Combine life sciences research expertise with hands-on Python skills to create and review rigorous scientific problems, analyze biological datasets, validate computational results, and improve advanced AI systems.
Combine life sciences research expertise with hands-on Python skills to create and solve scientific problems, analyze biological datasets, and review AI-related research solutions and code for accuracy and reproducibility.
Develop and validate radar-based precipitation algorithms and products using dual-polarization data, while analyzing radar variables, troubleshooting data quality issues, and collaborating on new platform capabilities.
Own computational and experimental protein-design work, improving models and closing the design-make-test loop through laboratory testing, kinetic analysis, and iterative design refinement.
