Founding Machine Learning Engineering Lead (Model Training) – Superintelligence Lab

About Us

We are the first system-level superintelligence lab, pioneering a paradigm shift in AI development through a scientifically grounded path toward superintelligence. Founded by industry veterans and internationally recognized academic leaders at the professor level, we are building next-generation intelligent systems to propel humanity toward its next civilizational leap. Learn more at https://eqe.ai.

As a Founding ML Engineer, you will be a core member of the technical team, working directly with our Head of AI Algorithms to design, implement, and scale training systems for next-generation 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 for non-standard LLM architectures, including looped and recurrent language 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, 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 principles into scalable, production-grade training systems.

Who You Are

• A senior-level 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.
• Ownership-driven, with a founder mindset: you take responsibility for outcomes, not just implementations.
• Comfortable operating in ambiguity and shaping systems that do not yet exist.

Big Plus

• Experience with model distillation and teacher-student training pipelines.
• Familiarity with recurrent or looped architectures, stateful models, or non-transformer approaches.
• Prior experience at frontier AI labs, hyperscalers, or deep-tech startups.
• Experience building or leading high-impact engineering teams.
• IDF 8200

Why Join Us

• Early team member, 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 at the professor level in AI, neuroscience-inspired computing, and advanced model architectures.
• 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 a small elite ML engineering leadership team as we fundraise.
• Meaningful equity ownership commensurate with founding-team responsibility and long-term impact.
• Top market salary level, cash compensation after funding round close in a few months, you may remain at your current job until then.
• 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.