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.
The role
This is a research role focused on building models that continuously evolve with the world. Our frontier work centers on efficiency, gradient-free exploration, real-time learning, and interface design. The most intelligent systems will increasingly be defined not just by what they know, but by how well they can interact with the world. For the first time, researchers who care deeply about intelligence and efficiency must also be obsessed with the interface through which a model acts.
Responsibilities
- Innovation: You have ideas and you move fast. You care about impact, not just effort.
- Cross-Stack Optimization: You collaborate across software, hardware, and algorithmic domains to unlock system-wide efficiency gains.
- Measure What Matters: The ultimate signal of value is real-world impact. Our current algorithms are capable of interacting with the world which is why product thinking is inseparable from research.
Qualifications
- A PhD or equivalent research experience in a computer science field
- 4-5+ years of industry experience
- Publishing record at ICML, NeurIPS, ACL, ICLR, EMNLP.
- 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
Benefits
- Flexible work: In-person collaboration in the Bay Area, a distributed global-first team, and team offsites.
- Adaption Passport: Annual travel stipend to explore a country you've never visited.
- Lunch Stipend: Weekly meal allowance for take-out or grocery delivery.
- Well-Being: Comprehensive medical benefits and generous paid time off.