Superintelligence Lab - Members of Technical Staff

Superintelligence Lab은 다음 영역에서 뛰어난 역량을 보유한 Members of Technical Staff (MTS)​를 채용합니다.

• Move between first-principles research and production-grade engineering.

• Pursue 0-to-1 ideas before they become obvious.

• Care deeply about rigor, speed, quality, and systems that actually work.

Preferred Qualifications

• • PhD or postdoctoral experience with strong engineering skills.

• • BS or MS degree with production-grade engineering and research experience.

• • Domain experts or winners of Olympiads and international competitions.

• Areas of Focus

• • Agentic Systems

• • Design agentic systems and harnesses for self-improvement.

• • Build long-term, episodic, and working memory systems through context engineering and optimization.

• • Develop evaluation and verification methods for multi-agent behavior.

• • Strong intuition and experience in reinforcement learning for foundation models and orchestrator training.

• • Multimodal Modeling

• • Experience in training VLMs, VLAs, image and video retrievers, and world models.

• • Develop multimodal agent systems and harnesses.

• • Conduct research on visual documents containing tables, charts, and graphs.

• • Develop multimodal evaluation methods for visual grounding, retrieval quality, embodied task execution, and complex multimodal tasks.

• • HAI & Evaluation

• • Strong experience across both AI and HCI.

• • Experience with evaluation problems and incentive design.

• • Research tacit knowledge and domain-specific workflows.

• • Hands-on experience with A/B testing and product design processes.

• • Inference & Efficiency

• • Expertise in neural architectures such as Mixture-of-Experts and large-scale AI systems.

• • Work on KV caching, speculative decoding, model routing, parallel decoding, long-context serving, retrieval latency optimization, and tool-call scheduling.

• • Design memory-efficient execution systems for agents operating across models, tools, retrievers, and environments.

• • Hands-on experience improving LLM serving frameworks such as vLLM and SGLang.