Founding Machine Learning Engineering Lead | Superintelligence Lab

About Us

We are a new superintelligence lab pioneering a paradigm shift in the development of AI systems through a scientifically feasible path to superintelligence. Founded by industry veterans and world-renowned academic professors. Learn more about us at https://eqe.ai

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You will be a core member of the founding technical team, working alongside the Head of AI Algorithms to design, implement, and scale training systems that enable new class of models beyond standard transformer architectures.

This is a hands-on role at the intersection of large-scale model training, systems engineering, and advanced model architectures. You will own the engineering foundation that enables novel looped and recurrent language models to scale efficiently, unlocking new capabilities in reasoning, efficiency, and deployability.

What You Will Do

• Architect and implement large-scale training systems (~500B parameters) for non-standard LLM architectures, including looped transformer models.
• Design and build parallelization and pipelining strategies to overcome back-propagation-through-time bottlenecks in recurrent models.
• Lead performance engineering efforts across the training stack, including memory optimization, kernel efficiency, and parallel execution.
• Work deeply with JAX (preferred) and/or PyTorch, optimizing distributed training across multi-node, multi-accelerator setups.
• Implement and optimize CUDA kernels where necessary to unlock performance beyond existing frameworks.
• Scale training to multi billion-parameter-class models, leveraging tensor, pipeline, and parallelism.
• Enable efficient distillation of large models into looped architectures and support fine-tuning on reasoning-focused datasets.
• Collaborate closely with the Head of AI Algorithms to translate new algorithmic methods into scalable, production-grade training systems.

Who You Are

• A senior-level applied ML engineer with deep hands-on experience training large language models (≥1B parameters).
• Strong background in distributed training, including tensor parallelism, pipeline parallelism, and performance bottleneck analysis.
• Comfortable working close to the metal: CUDA programming, memory optimization, and kernel-level performance tuning.
• Able to reason about trade-offs across compute, memory, throughput, and model quality.

Why Join Us

• High impact: This is a core early stage role. You will help define not just how we train models, but what becomes possible.
• Work with world-class talent: You will collaborate closely with some of the world's leading researchers and academics in AI.
• Real frontier work: We are not optimizing today's transformers, we are building the training systems that enable fundamentally new classes of models.
• Right-time entry: We are immediately pre-funding, offering a rare opportunity to join at maximum leverage, before scale-up and institutionalization.

Compensation & Equity

• The goal at the point is to assemble an elite ML engineering leadership team as we fundraise. You may remain at your current job until fundraising close.
• Meaningful equity ownership and a top-of-market salary, commensurate with founding-team responsibility and long-term impact.
• We are explicitly looking for individuals who are motivated by ownership, mission, and long-term impact.

Closing Note

Success in this role will not only help shape our company's future but also impact the future of humanity at large. If you are excited by the idea of building the systems that make next-generation intelligence possible, this role offers a rare opportunity to work at the frontier of AI with exceptional autonomy and responsibility.